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This work presents a complete workflow for the parameter identification of an MBS model representing a vibratory conveying system. First, the MBS model built within the multibody dynamics tool HOTINT is shown. Essential components, such as contact modeling and adaptive time step control, are explained in detail. The model includes a considerable amount of parameters that need to be identified. Altough, this model is fully deterministic, the complex contact dynamics can cause major effect on results by minor parameter variations. Thus, parameter variations usually show statistical variance, which is characteristic for real conveying processes too. For parameter identification it is important to detect parameters that significantly correlate with results. A method that allows to distinguish significant and non-significant parameters is presented. The application to the multibody system shows that stiffness, restitution and sliding friction of the contact model are the essential parameters. Next, the parameter identification is discussed with particular attention to multimodal distributions, that can occur in conveying systems. Furthermore, the integration of statistics into the deterministic simulation model is discussed. In order to demonstrate the applicability of the proposed methods, a parameter identification is performed based on measurement data of a single part on a real conveying system in the development site of STIWA Automation GmbH. Finally, the validity of the fully parametrized simulation model is shown by investigating geometric adjustments of the conveyor. In order to further verify the identified set of parameters, a conveying process with multiple parts is analyzed and compared in measurement and simulation. [ABSTRACT FROM AUTHOR]

There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders. The ZIV estimator, a novel Variational Bayes algorithm, enhances whole-brain imaging analysis for zero-inflated outcomes, revealing key neurocircuitry and greater variance for externalizing behaviors in the ABCD Study [ABSTRACT FROM AUTHOR]

Purpose: Quality of Life (QoL) is associated with a bandwidth of lifestyle factors that can be subdivided into fixed and potentially modifiable ones. We know too little about the role of potentially modifiable factors in comparison to fixed ones. This study examines four aspects of QoL and its associations with 15 factors in a sample of elderly primary care patients with a high risk of dementia. The main objectives are (a) to determine the role of the factors in this particular group and (b) to assess the proportion of fixed and potentially modifiable factors. Method: A high-risk group of 1030 primary care patients aged between 60 and 77 years (52.1% females) were enrolled in "AgeWell.de," a cluster-randomized, controlled trial. This paper refers to the baseline data. The multi-component intervention targets to decrease the risk of dementia by optimization of associated lifestyle factors. 8 fixed and 7 modifiable factors potentially influencing QoL served as predictors in multiple linear regressions. Results: The highest proportion of explained variance was found in psychological health and age-specific QoL. In comparison to health-related QoL and physical health, the modifiable predictors played a major role (corr. R2: 0.35/0.33 vs. 0.18), suggesting that they hold a greater potential for improving QoL. Conclusion: Social engagement, body weight, instrumental activities of daily living, and self-efficacy beliefs appeared as lifestyle factors eligible to be addressed in an intervention program for improving QoL. Trial registration: German Clinical Trials Register, reference number: DRKS00013555. Date of registration: 07.12.2017. [ABSTRACT FROM AUTHOR]

Purpose: To examine the parenthood desire, perceived parenthood stigma, and barriers to achieving parenthood among sexual minority men (SMM) in Canada, and to investigate factors influencing their fertility and assisted reproductive knowledge. Methods: Data were collected from March to mid-June 2023 using a 78-item anonymous online survey. Childless cisgender SMM (age 18+) living in Canada were recruited from the LGBTQIA+ community outside the fertility care networks. Chi-square, t-tests, ANOVA, reliability tests, Spearman's correlation, and hierarchical regression model were used for analysis. Results: Over 160 people clicked the survey hyperlink during the study period and 112 completed surveys were analyzed. The mean age of participants was 33.2±8.5 (range: 19.7–60.0). Having a child by any means was "quite"/"very" important to 35.7% (n=40), yet 56.0% (n=61) thought it was "unlikely" to achieve parenthood. Financial readiness (n=90, 85.7%) and relationship stability (n=86, 81.9%) were the two most "important" parenthood considerations. Participants who were non-white (p=0.017), under age 30 (p=0.008), and had no siblings (p=0.024) had significantly higher means of parenthood desire compared to others. The final hierarchical regression model explained 43% of the variance in the knowledge scores (R2adj =0.353), predicted by the levels of (i) education (β=0.37, p<0.001), (ii) family acceptance of sexual orientation (β=0.39, p=0.004), and (iii) parenthood desire (β=0.27, p=0.002). Conclusions: With an increasing number of SMM desiring children, it is pivotal to advance family-building equality through improving their fertility and assisted reproductive knowledge, removing disparities in accessing adoption and assisted reproductive services, and decreasing social stigma against SMM having children. [ABSTRACT FROM AUTHOR]

Subjective health ratings are associated with dementia risk such that those who rate their health more poorly have increased risk for dementia. The genetic and environmental mechanisms underlying this association are unclear, as prior research cannot rule out whether the association is due to genetic confounds. The current study addresses this gap in two samples of twins, one from Sweden (N = 548) and one from Denmark (N = 4,373). Using genetically-informed, bivariate regression models, we assessed whether additive genetic effects explained the association between subjective health and dementia risk as indexed by a latent variable proxy measure. Age at intake, sex, education, depressive symptomatology, and follow-up time between subjective health and dementia risk assessments were included as covariates. Results indicate that genetic variance and other sources of confounding accounted for the majority of the effect of subjective health ratings on dementia risk. After adjusting for genetic confounding and other covariates, a small correlation was observed between subjective health and latent dementia risk in the Danish sample (rE = −.09, p <.05). The results provide further support for the genetic association between subjective health and dementia risk, and also suggest that subjective ratings of health measures may be useful for predicting dementia risk. [ABSTRACT FROM AUTHOR]

Frailty is a complex trait. Twin studies and high-powered Genome Wide Association Studies conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from a Genome Wide Association Study on the Frailty Index to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples – the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67–84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty, measured via the FI, at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085–0.21) and 1.8% (β = 0.14, 95%CI, 0.10–0.17) of the variance, respectively, at age ~ 68/ ~ 70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~ 68/ ~ 70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies. [ABSTRACT FROM AUTHOR]

Although research shows a strong positive association between perceived stress and loneliness, the genetic and environmental etiology underlying their association remains unknown. People with a genetic predisposition to perceived stress, for example, may be more prone to feeling lonely and vice versa. Conversely, unique factors in people's lives may explain differences in perceived stress levels that, in turn, affect feelings of loneliness. We tested whether genetic factors, environmental factors, or both account for the association between perceived stress and loneliness. Participants were 3,066 individual twins (nFemale = 2,154, 70.3%) from the Washington State Twin Registry who completed a survey during April–May, 2020. Structural equation modeling was used to analyze the item-level perceived stress and loneliness measures. The correlation between latent perceived stress and latent loneliness was.68. Genetic and nonshared environmental variance components underlying perceived stress accounted for 3.71% and 23.26% of the total variance in loneliness, respectively. The genetic correlation between loneliness and perceived stress was.45 and did not differ significantly between men and women. The nonshared environmental correlation was.54 and also did not differ between men and women. Findings suggest that holding constant the strong genetic association between perceived stress and loneliness, unique life experiences underlying people's perceived stress account for individual differences in loneliness. [ABSTRACT FROM AUTHOR]

Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrixjacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Docynia delavayi (Franch.) Schneid. is a wild fruit tree that can be widely used for medicinal and edible purpose and has a high value of exploitation and utilization. We analyzed the growth traits of 62 half-sib families from 10 provenances in order to acquire more information about the genetic variance of growth traits in D. delavayi. Variation analysis, correlation analysis, principal component analysis, estimation of genetic parameters and comprehensive analysis were all done based on the data on growth traits. The results of the variance analysis revealed that all traits, with the exception of branch length, differed significantly or extremely significantly between provenances, whereas only leaf length did not differ significantly between families. The results of the correlation analysis showed that all growth traits, except for branch length and petiole length, had a significant or extremely significant correlation with the geo-climatic factors. Three principal components were extracted by principal component analysis; they were used as indicators for the comprehensive evaluation of D. delavayi from different provenances, and five excellent provenances were selected: Lancang (LC), Lianghe (LH), Shidian (SD), Yingjiang (YJ) and Xiangyun (XY). The results of genetic parameter estimation showed that all indicators except leaf length and leaf width had a high potential for genetic improvement. Therefore, five traits (seedling height (SH), ground diameter (GD), branch length (BL), petiole length (PL) and the total number of leaves (TNL)) were used as comprehensive index to select superior families, and a total of 20 superior families were selected. This study will lay a theoretical foundation and practical guidance for the genetic improvement of D. delavayi. [ABSTRACT FROM AUTHOR]

Assortative mating – the non-random mating of individuals with similar traits – is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities. Non-random mating can complicate genetic studies, but implications hinge on its history in prior generations. Here, the authors use genetic similarity between relatives to investigate which traits show evidence of recent changes in mating behavior. [ABSTRACT FROM AUTHOR]

The contributions of height, weight and skeletal age (SA) to strength and motor performances of male soccer players 9–12 (n = 60) and 13–16 (n = 52) years were estimated. SA was assessed with the Fels method, and was expressed as the standardized residual of the regression of SA on chronological age CA (SAsr). Static strength (right + left grip), speed (5 m, 20 m sprints), acceleration (10 to 20 m), agility (figure-of-eight run), explosive strength (vertical jump) and endurance (yo–yo intermittent shuttle run, 13–16 years only) were measured. Hierarchical multiple regression was used. The interaction of SAsr with body size (height and height x weight interaction) explained most of the variance in strength in both age groups, 9–12 years (51.6%) and 13–16 years (56.7%), and in speed (31.4%, 38.7%), acceleration (39.6%), and explosive strength (32.6%) among players 13–16 years. In contrast, SAsr alone explained limited amounts of variance in strength, speed, acceleration and vertical jump among players 9–12 years (1.4–4.5%) and 13–16 years (0–0.5%). Results for agility varied with CA group, while SAsr per se was the primary contributor to endurance among players 13–16 years (18.5% of the variance). Although the influence of body size and skeletal maturity status on performances was significant, the explained variance differed among tasks and between CA groups, and suggested a role for other factors affecting performances of the soccer players. [ABSTRACT FROM AUTHOR]

The estimation of heritability is a common practice in the field of ecology and evolution. Heritability of the traits is often estimated using one single measurement per individual, although many traits (especially behavioural and physiological traits) are characterized by large within-individual variance, and ideally a large number of within individual measurements can be obtained. Importantly, the effect of the within-individual variance and the rate at which this variance is sampled on the estimation of heritability has not been thoroughly tested. We fill this gap of knowledge with a simulation study, and assess the effect of within- and between-individual sample size, and the true value of the variance components on the estimation of heritability. In line with previous studies we found that the accuracy and precision of heritability estimation increased with sample size and accuracy with higher values of additive genetic variance. When the sample size was above 500 accuracy and power of heritability estimates increased in the models including repeated measurements, especially when within-individual variance was high. We thus suggest to use a sample of more than 100 individuals and to include more than two repeated measurements per individual in the models to improve estimation when investigating heritability of labile traits. Significance statement: Heritability reflects the part of the trait's phenotypic variation underlined by genetic variation. Despite the difficulties of heritability calculation (high number of individuals is needed with known relatedness), it is a widely used measure in evolutionary studies. However, not every factor potentially affecting the quality of heritability estimation is well understood. We thus investigated with a comprehensive simulation study how the number of repeated measurements per individuals and the amount of within-individual variation influence the goodness of heritability estimation. We found that although the previously described effect of the number of studied individuals was the most important, including repeated measurements also improved the reliability of the heritability estimates, especially when within-individual variation was high. Our results thus highlight the importance of including repeated measurements when investigating the heritability of highly plastic traits, such as behavioural or physiological traits. [ABSTRACT FROM AUTHOR]

Many existing extremal data span only a few decades that result in large uncertainty in the estimated shape parameter of the extreme hazard model. This in turn leads to potentially large bias and variance of extrapolated extreme values at high return levels. This paper illustrates a statistical method that helps obtain hazard models that produce estimates at high return levels with reduced bias and variance. This method makes use of the maximum recorded values of extremal data that are independently recorded from a number of observational sites or generated by heterogeneous mechanisms. The logarithmically transformed average recurrence interval of the maximum recorded value at a site is shown to follow the Gumbel (Type I extreme value) distribution; therefore, multiple, say m , observational sites provide a sample of size m log-transformed average recurrence intervals, each from a distinct site. The sample can be treated as a sample from a Gumbel distribution, irrespective of the underlying hazard-generating mechanisms or the statistical hazard models. Application of the method is illustrated by analysis of the extreme wind gust data from automatic weather stations in South Australia and shown to lead to significant reduction of 2.9 m/s in potential bias of the mean and a reduction of 98.1% in variance of the 500-year gust speed at Adelaide Airport. The results are compared to the specifications in the Australian standard AS/NZS 1170.2:2021 and indicate that the standard may have overestimated the wind gust hazard; hence, the specified design wind speeds may fall on the conservative side for South Australia. [ABSTRACT FROM AUTHOR]

The aesthetic values that individuals place on visual images are formed and shaped over a lifetime. However, whether the formation of visual aesthetic value is solely influenced by environmental exposure is still a matter of debate. Here, we considered differences in aesthetic value emerging across three visual domains: abstract images, scenes, and faces. We examined variability in two major dimensions of ordinary aesthetic experiences: taste-typicality and evaluation-bias. We build on two samples from the Australian Twin Registry where 1547 and 1231 monozygotic and dizygotic twins originally rated visual images belonging to the three domains. Genetic influences explained 26% to 41% of the variance in taste-typicality and evaluation-bias. Multivariate analyses showed that genetic effects were partially shared across visual domains. Results indicate that the heritability of major dimensions of aesthetic evaluations is comparable to that of other complex social traits, albeit lower than for other complex cognitive traits. The exception was taste-typicality for abstract images, for which we found only shared and unique environmental influences. Our study reveals that diverse sources of genetic and environmental variation influence the formation of aesthetic value across distinct visual domains and provides improved metrics to assess inter-individual differences in aesthetic value. A twin study reveals genetic effects on variability in major dimensions of aesthetic evaluations are shared across visual domains, whereas only environmental effects influence aesthetic tastes for abstract images. [ABSTRACT FROM AUTHOR]

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains. [ABSTRACT FROM AUTHOR]

To date, the assessment of a simultaneous effect of SNPs on manifesting aggression via polygenic score (PGS) approach has been performed mainly in Western Europeans and is scarce in Russians. In turn, genes belonging to monoaminergic systems, inflammatory response, hypothalamic-pituitary-adrenal axis, telomerase activity, and miRNA regulation have been previously associated with aggressive behavior or affective pathology. Therefore, we aimed to estimate a combined effect of PGS based on 30 SNPs belonging to abovementioned systems and social/lifestyle factors on individual differences in BPAQ-measured aggression in young adults from the Volga-Ural region (VUR) of Russia. Initially, a series of multiple linear regression was carried out in the testing sample (N = 500) from VUR with PGS calculated on a basis of effect estimates obtained from the training sample (N = 565) from VUR and controlling for sex, ethnicity, and age. The final model was based on a combined effect of PGS of TERT, TNF, SLC6A4, smoking and maternal protection (p = 8.41 × 10–10), which explained up to 11.51% of variance in physical aggression. Subsequently, we calculated PGS in the total sample from VUR (N = 1065) based on summary statistics from risky behavior GWAS conducted in UK Biobank (Mbatchou et al., 2021). The best model explaining up to 4.6% of variance in verbal aggression comprised of PGS, sibship size, and childhood adversity (p = 1.71 × 10–6). Revealed findings evidence in a better prognostic ability of models comprising PGS based on summary statistics from ethnically same cohort and the same phenotype. [ABSTRACT FROM AUTHOR]

Many quantitative genetic models assume that all genetic variation is additive because of a lack of data with sufficient structure and quality to determine the relative contribution of additive and non-additive variation. Here the fractions of additive (fa) and non-additive (fd) genetic variation were estimated in Sitka spruce for height, bud burst and pilodyn penetration depth. Approximately 1500 offspring were produced in each of three sib families and clonally replicated across three geographically diverse sites. Genotypes from 1525 offspring from all three families were obtained by RADseq, followed by imputation using 1630 loci segregating in all families and mapped using the newly developed linkage map of Sitka spruce. The analyses employed a new approach for estimating fa and fd, which combined all available genotypic and phenotypic data with spatial modelling for each trait and site. The consensus estimate for fa increased with age for height from 0.58 at 2 years to 0.75 at 11 years, with only small overlap in 95% support intervals (I95). The estimated fa for bud burst was 0.83 (I95=[0.78, 0.90]) and 0.84 (I95=[0.77, 0.92]) for pilodyn depth. Overall, there was no evidence of family heterogeneity for height or bud burst, or site heterogeneity for pilodyn depth, and no evidence of inbreeding depression associated with genomic homozygosity, expected if dominance variance was the major component of non-additive variance. The results offer no support for the development of sublines for crossing within the species. The models give new opportunities to assess more accurately the scale of non-additive variation. [ABSTRACT FROM AUTHOR]

Genetic parameters for growth, trunk quality, and susceptibility to frost and Sphaerulina musiva attack were estimated from 34 half-sib families of hybrid poplar from the crossing of non-native parents, Populus maximowiczii A. Henry, and Populus trichocarpa Torr. & Gray, 3 and 6 years after planting. The use of spatial analysis proved to be the best method for quantitative growth data. The proportion of the among-family variance to the total (phenotypic) variance as well as the high heritabilities of growth and susceptibility to frost and Spaherulina musiva showed a high potential for selection for these traits while the quality traits were under low genetic control. Some families showed gains for several traits, suggesting the possibility of developing a selection index to obtain superior families that show gain for not only growth but quality and adaptive traits as well. Type B correlations were high, suggesting that families responded in the same way regardless of the site. High type A correlation between growth traits at 3 and 6 years showed early selection potential, although these relationships should be confirmed with future measurements to evaluate this effect at maturity. These results can be integrated into the strategy for improving hybrid poplar parental populations and, in the longer term, will make it possible to optimize the selection of individuals with traits of interest for the operational deployment of hybrid poplar clones. [ABSTRACT FROM AUTHOR]

In this paper, we construct three types of augmented samples, which are samples generated from two separate randomization events. The first type combines a simple random sample (SRS) with a spatially balanced sample (SBS) selected from the same finite population. The second type combines an SBS with an SRS. The third type combines two spatially balanced samples. The simple random sample is constructed without replacement and does not contain any ties. The spatially balanced samples are constructed using the properties of the Halton sequence. We provide the first and second order inclusion probabilities for the augmented samples. Next, using the inclusion probabilities of the augmented samples, we construct estimators for the mean and total of a finite population. The efficiency of the augmented samples varies between the efficiency of SRS and SBS samples. If the number of SRS observations in the augmented sample is large, the efficiency is closer to the efficiency of SRS. Otherwise, it is closer to the efficiency of SBS. We also provide estimators for the variances of the estimators of population total of augmented samples. The stability of these variance estimators depends on the proportion of SRS observations in the augmented samples. The larger number of SRS observations lead to stable variance estimators. [ABSTRACT FROM AUTHOR]

Purpose: Despite advances in cancer treatment, there is a prevalence of pediatric childhood cancer survivors still at risk of developing adverse disease and treatment outcomes, even after the end of treatment. The present study aimed to (1) explore how mothers and fathers assess the health-related quality of life (HRQoL) of their surviving child and (2) evaluate risk factors for poor parent-reported HRQoL in childhood cancer survivors about 2.5 years after diagnosis. Methods: We assessed parent-reported HRQoL of 305 child and adolescent survivors < 18 years diagnosed with leukemia or tumors of central nervous system (CNS) with the KINDL-R questionnaire in a prospective observational study with a longitudinal mixed-methods design. Results: In agreement with our hypotheses, our results show that fathers rate their children's HRQoL total score as well as the condition-specific domains family (p =.013, d = 0.3), friends (p =.027, d = 0.27), and disease (p =.035, d = 0.26) higher than mothers about 2.5 years after diagnosis. Taking variance of inter-individual differences due to family affiliation into account, the mixed model regression revealed significant associations between the diagnosis of CNS tumors (p =.018, 95% CI [− 7.78, − 0.75]), an older age at diagnosis, (p =.011, 95% CI [− 0.96, − 0.12]), and non-participation in rehabilitation (p =.013, 95% CI [− 10.85, − 1.28]) with poor HRQoL in children more than 2 years after being diagnosed with cancer. Conclusion: Based on the results, it is necessary for health care professionals to consider the differences in parental perceptions regarding children's aftercare after surviving childhood cancer. High risk patients for poor HRQoL should be detected early, and families should be offered support post-cancer diagnosis to protect survivors' HRQoL during aftercare. Further research should focus on characteristics of pediatric childhood cancer survivors and families with low participation in rehabilitation programs. [ABSTRACT FROM AUTHOR]

Metabolic rate correlates with function of the individual, such as foraging patterns and swimming performance. In this study, genetic variation in the routine metabolic rate (RMR) and a correlation between RMR and swimming speed were examined in medaka Oryzias latipes. We made nine full-sib families and estimated genetic variance using laboratory-reared individuals originating from a wild population. RMR was calculated using the average of three consecutive measurements in an individual (mean ± SD = 2.28 ± 0.71 μg O2/min, range 1.04–3.94, n = 36). Values of RMR differed significantly among families, even after adjusting for the effect of body weight with a linear mixed model. The estimations of RMR showed a 1.4-fold difference between the minimum values for a family (mean, 95% C.I. = 1.80, 1.36–2.37 μg O2/min) and the maximum values (mean, 95% C.I. = 2.52, 1.88–3.37 μg O2/min). In addition, RMR and swimming speed were positively correlated, even after adjusting for the effects of standard length, sex, and families. We consider that correlation between RMR and swimming speed may have a genetic basis. Selection on swimming performance could act as a selection pressure to increase metabolic rates of this species. [ABSTRACT FROM AUTHOR]

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 – 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data. Identification of gene-by-environment interactions is crucial to understand the interplay of environmental effects on complex traits. Here, the authors present MonsterLM, a method for estimating the proportion of trait variance explained by gene-by-environment interactions in a fast, unbiased manner on biobank-scale datasets. [ABSTRACT FROM AUTHOR]

Purpose: To examine surrogates' mental health, social support, and relationship with intended parents (IPs) during the COVID-19 pandemic from March 2020 to February 2022. Methods: Data were collected between April 29, 2022 and July 31, 2022, at an academic IVF center in Canada using an 85-item online anonymous cross-sectional survey that included three standardized scales measuring mental health (PHQ-4), loneliness, and social support. Eligible surrogates actively involved in surrogacy during the study period received email invitations. Results: The response rate was 50.3% (338/672); 320 submitted surveys were analyzed. Two-thirds (65%) of respondents experienced mental health concerns during the pandemic and were significantly less comfortable about seeking mental health support than those without concerns. Nonetheless, 64% were highly satisfied with their surrogacy experience; 80% received a high level of support from their IPs, and 90% reported a good relationship with them. The final hierarchical regression model identified five significant predictors, explaining 39.4% of the variance in PHQ-4 scores: a prior mental health history, COVID-19 impact on personal life, surrogacy satisfaction, loneliness, and social support. Conclusions: COVID-19 created an unprecedented challenge to surrogacy care, increasing surrogates' risk of experiencing mental health symptoms. Our data show that IP support and the surrogate-IP relationship were fundamentals to surrogacy satisfaction. The findings are relevant to fertility and mental health practitioners in identifying surrogates who are more susceptible to mental health challenges. Fertility clinics should ensure adequate psychological screening of surrogate candidates and proactively offer mental health support services. [ABSTRACT FROM AUTHOR]

Understanding trait variation along environmental gradients is crucial for assessing the adaptive potential of species. We analysed the intraspecific variation in six morphological traits of Frullania ericoides across environmental gradients (elevation, canopy openness and distance to water sources) in a humid ecological refuge in Brazil's semi-arid region. The traits measured (mean and coefficient of variation) were: leaf lobe area, leaf lobule area, underleaf area, stem width, ratio between leaf lobule area and leaf lobe area, and ratio between the number of laminate lobules and the total number of lobules. Seventy specimens from eleven localities (populations) showed greater intrapopulation than interpopulation mean trait variation. All traits, except for the ratio between leaf lobule area and leaf lobe area, presented interpopulation differences. Two trait strategies emerged: one related to water storage and plant size, and the other linked to proportional increases in leaf lobule area and lobe area. The lower the elevation was, the higher were the mean values of leaf lobe area and leaf lobule area, and the variance of stem width. The species exhibited significant trait variation, which can be decisive in the responses of populations to new selective pressures of establishment and maintenance. [ABSTRACT FROM AUTHOR]

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample. [ABSTRACT FROM AUTHOR]

Iron is essential for many biological processes, but iron levels must be tightly regulated to avoid harmful effects of both iron deficiency and overload. Here, we perform genome-wide association studies on four iron-related biomarkers (serum iron, serum ferritin, transferrin saturation, total iron-binding capacity) in the Trøndelag Health Study (HUNT), the Michigan Genomics Initiative (MGI), and the SardiNIA study, followed by their meta-analysis with publicly available summary statistics, analyzing up to 257,953 individuals. We identify 123 genetic loci associated with iron traits. Among 19 novel protein-altering variants, we observe a rare missense variant (rs367731784) in HUNT, which suggests a role for DNAJC13 in transferrin recycling. We further validate recently published results using genetic risk scores for each biomarker in HUNT (6% variance in serum iron explained) and present linear and non-linear Mendelian randomization analyses of the traits on all-cause mortality. We find evidence of a harmful effect of increased serum iron and transferrin saturation in linear analyses that estimate population-averaged effects. However, there was weak evidence of a protective effect of increasing serum iron at the very low end of its distribution. Our findings contribute to our understanding of the genes affecting iron status and its consequences on human health. A GWAS on the four major iron-related biomarkers is conducted using data from the Trøndelag Health Study (HUNT), Michigan Genomics Initiative (MGI), and SardiNIA study, to identify 123 loci associated with iron homeostasis and effect on all-cause mortality. [ABSTRACT FROM AUTHOR]

Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene–environment and gene–gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well. [ABSTRACT FROM AUTHOR]

In genomewide association study (GWAS) of a complex phenotype, a large number of variants, many with small effect sizes, are found to contribute to the variability of the phenotype. Subsequent to the identification of such variants in a GWAS, it is of interest to estimate the risk jointly conferred by the variants. We propose three different strategies of combining the risk SNPs to calculate an allele dosage score. Using simulations, we evaluate the different measures of allele dosage score with respect to the risk prediction accuracy of a binary trait and the proportion of variance explained for a quantitative trait. For a binary trait, an allele dosage score defined based on log odds ratio performs marginally better than the other two measures. For a quantitative trait, the measure based on the standardized slope coefficient in linear regression of the trait on single-nucleotide polymorphism (SNP) genotypes performs better than the measures using the weights proportional to log P-value and the proportion of variance explained. We demonstrate the utility of these measures using a real data on type 2 diabetes and fasting blood sugar level in a south Indian population. [ABSTRACT FROM AUTHOR]

Owing to sex-specific reproductive strategies, the mean and variance in annual offspring production may differ between the sexes. In addition, there may be sex-specific changes in reproductive performance with age (e.g. senescence). We used 20 and 50 years of longitudinal data on male and female yellow-bellied marmots, respectively, to investigate sex-specific age effects and genetic variance in annual reproductive success. In both sexes, annual offspring production increased linearly with age until a peak was reached at 7 years. This was followed by a decline in annual offspring production in both sexes, indicative of reproductive senescence. However, the initial increase and the subsequent decline (senescence) in reproductive success were both faster in males compared to females. Genetic variance in annual offspring production was higher in males than in females, but heritability was low for both sexes. Additionally, we found no cross-sex genetic correlation in the number of offspring produced, possibly reflecting sex-specific selection related to the inter-sexual differences in reproductive strategies. There was an effect of year on annual offspring production in both sexes, with a high yearly correlation between the sexes emphasizing the importance of environmental variation in determining fitness. Overall, these results demonstrate the impact of sex-specific reproductive strategies on annual offspring production and suggest that male and female marmots may be evolving to separate phenotypic optima. This study further demonstrates the value and limitations of long-term studies investigating sex-based patterns of ageing in the wild. Significance: This study on yellow-bellied marmots demonstrates the key constraints surrounding the research of age-related changes in reproduction in wild populations, which is especially challenging in males. Among these challenges are high immigration rates which prevent the estimation of exact age and maternal identity. Genetic analysis—the only reliable way to estimate paternity—has only been a recent development, limiting the number of father-son relationships available for analysis. The dataset used in this study is long term (20 years of data on males and 50 on females), partially overcoming these obstacles. Results show that reproductive success declines with age in both sexes, with a faster rate of decline in males compared to females, and is influenced by the environment. Genetically, male and female reproductive success is not correlated, revealing that both sexes may be following separate evolutionary trajectories. [ABSTRACT FROM AUTHOR]

Various indices have been reported regarding heart rate variability (HRV), but many of them correlate to each other, suggesting the existence of the underlying common factors. We tried to extract factors underlying HRV indices and investigated their features. Using big data of 24-h electrocardiogram (ECG) called Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR), we calculated 4 time-domain, 4 frequency-domain, and 2 nonlinear HRV indices and the amplitude of cyclic variation of heart rate (Acv) in 113,793 men and 140,601 women with sinus rhythm ECG. Factor analysis revealed that there were two factors with eigenvalue ≥ 1 by which 91% of variance among the HRV indices was explained. Factor 1 that was strongly contributed by very-low frequency, low frequency (LF), and high frequency (HF) components and Acv and it increased with age from 0 to 20 year, then decreased until 65 year, and increased slightly after 80 year. It also increased with daily physical activity at the mild level of activity. Factor 2 that was contributed strongly by scaling exponent α1 and LF-to-HF ratio increased with age until 35 year, plateaued between 35 and 55 year, and decreased thereafter. It also increased with mild to moderate physical activity. HRV indices are constituted by two common factors relating to cardiac vagal function and complexity of heart rate dynamics, respectively, which differ in the relationships with age and physical activity from each other. Although many indices have been proposed for HRV, their constituent factors may be a few. [ABSTRACT FROM AUTHOR]

Reduced volumes in brain regions of interest (ROIs), primarily from adult samples, are associated with posttraumatic stress disorder (PTSD). We extended this work to children using data from the Adolescent Brain Cognitive Development (ABCD) Study® (N = 11,848; Mage = 9.92). Structural equation modeling and an elastic-net (EN) machine-learning approach were used to identify potential effects of traumatic events (TEs) on PTSD symptoms (PTSDsx) directly, and indirectly via the volumes 300 subcortical and cortical ROIs. We then estimated the genetic and environmental variation in the phenotypes. TEs were directly associated with PTSDsx (r = 0.92) in children, but their indirect effects (r < 0.0004)—via the volumes of EN-identified subcortical and cortical ROIs—were negligible at this age. Additive genetic factors explained a modest proportion of the variance in TEs (23.4%) and PTSDsx (21.3%), and accounted for most of the variance of EN-identified volumes of four of the five subcortical (52.4–61.8%) three of the nine cortical ROIs (46.4–53.3%) and cerebral white matter in the left hemisphere (57.4%). Environmental factors explained most of the variance in TEs (C = 61.6%, E = 15.1%), PTSDsx (residual-C = 18.4%, residual-E = 21.8%), right lateral ventricle (C = 15.2%, E = 43.1%) and six of the nine EN-identified cortical ROIs (C = 4.0–13.6%, E = 56.7–74.8%). There is negligible evidence that the volumes of brain ROIs are associated with the indirect effects of TEs on PTSDsx at this age. Overall, environmental factors accounted for more of the variation in TEs and PTSDsx. Whereas additive genetic factors accounted for most of the variability in the volumes of a minority of cortical and in most of subcortical ROIs. [ABSTRACT FROM AUTHOR]

Aesthetic chills, broadly defined as a somatic marker of peak emotional-hedonic responses, are experienced by individuals across a variety of human cultures. Yet individuals vary widely in the propensity of feeling them. These individual differences have been studied in relation to demographics, personality, and neurobiological and physiological factors, but no study to date has explored the genetic etiological sources of variation. To partition genetic and environmental sources of variation in the propensity of feeling aesthetic chills, we fitted a biometrical genetic model to data from 14,127 twins (from 8995 pairs), collected by the Netherlands Twin Register. Both genetic and unique environmental factors accounted for variance in aesthetic chills, with heritability estimated at 0.36 ([0.33, 0.39] 95% CI). We found females more prone than males to report feeling aesthetic chills. However, a test for genotype x sex interaction did not show evidence that heritability differs between sexes. We thus show that the propensity of feeling aesthetic chills is not shaped by nurture alone, but it also reflects underlying genetic propensities. [ABSTRACT FROM AUTHOR]

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% – 23% higher than corrected estimates using UK Biobank data. Methods for estimating heritability, the fraction of variance attributable to genetic factors, assume random mating. Here, the authors show that under assortative mating, when mate choice reflects phenotypic similarity, these methods produce overestimates. [ABSTRACT FROM AUTHOR]

This study aimed to explore the prevalence of sleep disturbances among preschool children in rural areas of China and identify the underlying associated factors. A cross-sectional study was conducted in rural areas of Anhui Province, China, from September 2019 to January 2020. The caregivers of children from 26 kindergartens completed the demographic questionnaire, the Children's Sleep Habits Questionnaire (CSHQ), the Strengths and Difficulties Questionnaire (SDQ), the Self-rating Anxiety Scale (SAS), and the adapted Identification and Management of Feeding Difficulties (IMFeD) tool. One-way analysis of variance, independent-samples t-test, and hierarchical multiple linear regression were conducted to explore the potential influencing factors of sleep disturbances in preschool children. For the specific sleep disturbances based on each subscale of the CSHQ, bedtime resistance (54.9%) and sleep anxiety (47.9%) were the most common types of sleep disturbances, followed by daytime sleepiness (21.3%), sleep duration (20.8%), parasomnias (16.5%), sleep-onset delay (12.6%), night wakings (12.2%) and sleep-disordered breathing (10.3%). The ages of children, children's emotional/behavioral problems, children's feeding difficulties, inconsistent parenting attitudes of parents, poor parenting styles, non-parent caregivers, and caregiver anxiety contributed significantly to the CSHQ total score, accounting for approximately 27.1% (R2 = 0.271) of the variance. Our study indicates that the prevalence of sleep disturbances among preschool children in rural areas of China is quite high. Furthermore, the potential risk factors are complicated, including factors related to both children and their caregivers. [ABSTRACT FROM AUTHOR]

Writer identification (WI) is a typical pattern recognition problem with the goal of recognizing the writer of a text from images of his or her handwriting. For handwriting-based applications, a new approach is required that can confirm the writer based on a very small amount of available text. However, due to the limited number of characters and the diverse contents of the available samples, it is still challenging to achieve a high recognition rate on large-scale databases. To solve this problem, this paper proposes an efficient method for offline text-independent WI using redundant writing patterns and dual-factor analysis of variance (DF-ANOVA). In this method, a search for writing patterns in a limited number of handwritten texts is first conducted to generate meaningful codebooks. Then, these writing patterns are described by means of the simplified Wigner distribution function (WDF) and improved directional index histogram (DIH) descriptors to extract both structural and texture features, and the two corresponding feature distances are integrated by means of a fuzzy integral rule to make the final decision. The Fisher discriminant ratio (FDR) is used for feature selection, and DF-ANOVA is used to eliminate the influence of factors associated with the labels of samples on the feature distance. The results of evaluations on the IAM (96.92%), Firemaker (96.4%), and Uyghur2016 (100%) databases illustrate that this system shows strong robustness to an increasing number of writers and a decreasing number of words in the sample texts. [ABSTRACT FROM AUTHOR]

In this paper, a multivariate heterogeneous variance components model was developed which allows for determination of location specific variance components in the analysis of multiple related traits. In addition to spatial heterogeneity, genetic similarities are also considered by assigning genetic variance components. The performance of the developed model was evaluated through an extensive simulation study and comparison of models was conducted by heritability estimations. The simulation study reveals that the developed method can control the locational heterogeneity well and the heritability estimations are close to desired proportions for the developed model. A real plant breeding data set was used for illustration. [ABSTRACT FROM AUTHOR]

Key message: The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology. [ABSTRACT FROM AUTHOR]

For many phenotypes, individual scores are obtained as the parameter estimates of person-level models fit to intensive repeated measures from physiological sensors or experience sampling studies. Biometrical genetic analysis of such phenotypes is often done in a 2-step sequence: first the phenotypic parameters are estimated for each individual, then classical twin modeling is used to partition their variance. This study demonstrates deficiencies in accuracy and statistical power of the two-step approach to estimate genetic signals and advocates for the use of hierarchical models to overcome both problems. Simulations are used to demonstrate the benefits to accuracy and statistical power from a hierarchical modeling approach. A model of heart rate fluctuations was applied to experimental data from twin pairs recorded in independent trials. Results of the data application reveal moderate but uncorrelated heritabilities for two parameters of heart rate: oscillation frequency and damping ratio. By merging biometrical genetic analysis with process models, hierarchical mixed-effects modeling has potential to assist with discovery and extraction of novel phenotypes from within-person data and to validate theoretical models of within-person processes. [ABSTRACT FROM AUTHOR]

Angelman syndrome (AS) is one of the common genetic disorders that could emerge either from a 15q11–q13 deletion or paternal uniparental disomy (UPD) or imprinting or UBE3A mutations. AS comes with various behavioral and phenotypic variability, but the acquisition of subjects for experiment and automating the landmarking process to characterize facial morphology for Angelman syndrome variation investigation are common challenges. By automatically detecting and annotating subject faces, we collected 83 landmarks and 10 anthropometric linear distances were measured from 17 selected anatomical landmarks to account for shape variability. Statistical analyses were performed on the extracted data to investigate facial variation in each age group. There is a correspondence in the results achieved by relative warp (RW) of the principal component (PC) and the thin-plate spline (TPS) interpolation. The group is highly discriminated and the pattern of shape variability is higher in children than other groups when judged by the anthropometric measurement and principal component. [ABSTRACT FROM AUTHOR]

The contrasting habitats providing a heterogenous environment are considered to affect morphological traits of Lepidoptera due to their adaptability to various selection pressures under such conditions. The morphological and genetic variability of the butterfly Argynnis paphia (Nymphalidae) were studied with a focus on populations across different environments of the Western Carpathians. Male individuals were collected from five sites differing in elevation, type of bedrock, vegetation and climatic region. Morphometric analyses indicated significant variability in reproductive (genitalia width) and non-reproductive (forewing shape and size) traits. The most significant differences were recorded between the locality Bačová, representing the highest elevation, and other localities. The statistical analyses revealed the correlation of wing morphology with elevation, bedrock type and annual average temperature, while morphological differences of valvae structures were correlated with altitude. We concluded that size and shape of wings but also male genitalia vary among populations of A. paphia from different regions in response to environmental conditions. [ABSTRACT FROM AUTHOR]

Hot-wire probe resolution effects on the measurements of the streamwise velocity fluctuation are assessed in a two-dimensional fully rough wall turbulent boundary layer (TBL). Various viscous-scaled wire lengths l + ranging from 24 to 168 are used. It is found that, in contrast to results observed in smooth wall TBLs measurements, there is no effect of the wire length on the measurements. Accordingly, no corrections similar to those required for smooth wall TBLs are necessary for fully rough TBLs. In fact, the application of such corrections to fully rough wall TBL measurements can lead to erroneous near-wall behaviour of the variance of the longitudinal velocity component. [ABSTRACT FROM AUTHOR]

A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that 'suboptimal' is different from 'slow' at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research. [ABSTRACT FROM AUTHOR]

The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, p 80 % , while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution. [ABSTRACT FROM AUTHOR]

Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions. [ABSTRACT FROM AUTHOR]

We effortlessly interact with objects in our environment, but how do we know where something is? An object's apparent position does not simply correspond to its retinotopic location but is influenced by its surrounding context. In the natural environment, this context is highly complex, and little is known about how visual information in a scene influences the apparent location of the objects within it. We measured the influence of local image statistics (luminance, edges, object boundaries, and saliency) on the reported location of a brief target superimposed on images of natural scenes. For each image statistic, we calculated the difference between the image value at the physical center of the target and the value at its reported center, using observers' cursor responses, and averaged the resulting values across all trials. To isolate image-specific effects, difference scores were compared to a randomly-permuted null distribution that accounted for any response biases. The observed difference scores indicated that responses were significantly biased toward darker regions, luminance edges, object boundaries, and areas of high saliency, with relatively low shared variance among these measures. In addition, we show that the same image statistics were associated with observers' saccade errors, despite large differences in response time, and that some effects persisted when high-level scene processing was disrupted by 180° rotations and color negatives of the originals. Together, these results provide evidence for landmark effects within natural images, in which feature location reports are pulled toward low- and high-level informative content in the scene. [ABSTRACT FROM AUTHOR]

The inter-session Intraclass Correlation Coefficient (ICC) is a commonly investigated and clinically important metric of reliability for pressure pain threshold (PPT) measurement. However, current investigations do not account for inter-repetition variability when calculating inter-session ICC, even though a PPT measurement taken at different sessions must also imply different repetitions. The primary aim was to evaluate and report a novel metric of reliability in PPT measurement: the inter-session-repetition ICC. One rater recorded ten repetitions of PPT measurement over the lumbar region bilaterally at two sessions in twenty healthy adults using a pressure algometer. Variance components were computed using linear mixed-models and used to construct ICCs; most notably inter-session ICC and inter-session-repetition ICC. At 70.1% of the total variance, the source of greatest variability was between subjects ( σ subj 2 = 222.28 N2), whereas the source of least variability (1.5% total variance) was between sessions ( σ sess 2 = 4.83 N2). Derived inter-session and inter-session-repetition ICCs were 0.88 (95%CI: 0.77 to 0.94) and 0.73 (95%CI: 0.53 to 0.84) respectively. Inter-session-repetition ICC provides a more conservative estimate of reliability than inter-session ICC, with the magnitude of difference being clinically meaningful. Quantifying individual sources of variability enables ICC construction to be reflective of individual testing protocols. [ABSTRACT FROM AUTHOR]

Interspecies interactions have a profound influence on spatial distribution of coexisting microbial species. We explored whether spatial variance of species distribution (SVSD) predicts the degree of interspecies interactions within a microbial metacommunity. Simulations were used to determine the relationships from random, lake, soil, and biofilm metacommunity datasets (1,000 times). All of the bacterial datasets showed a negative correlation between the habitat breadth (inverse to SVSD) and the numbers of total, positive, and negative interspecies interactions (P < 0.05); the only exception was the relationship between habitat breadth and negative interactions in the biofilm dataset. The random dataset had no significant relationships (P > 0.05). We repeated the simulations to determine the degree of correlation and reproducibility (100 times). Habitat breadth was negatively correlated with the total and positive interactions in all of the real datasets (P < 0.05), and the negative relationships persisted across repetitions. Despite variability in the slope of total interactions, the slope values of positive interactions were similar for the real datasets (− 19.9, − 19.2, and − 25.8 for lake, soil, and biofilm, respectively). In conclusion, our results demonstrate the patterns of species interaction–distribution and show that interspecies interactions are positively correlated with the SVSD. [ABSTRACT FROM AUTHOR]

Mixed geographically weighted regression (MGWR) models are a useful tool to model a regression relationship where the impact of some explanatory variables on the response variable is global and that of the others is spatially varying. The existing estimation methods for MGWR models assume that the model errors are homoscedastic. However, heteroscedasticity is very common in geo-referenced data and ignoring heteroscedasticity may cause efficiency loss on the coefficient estimates. In this paper, we propose a re-weighting estimation method for heteroscedastic MGWR models, in which the variance function of the model errors is estimated by the kernel method with an adaptive bandwidth and the coefficients are re-estimated based on the weighted observations. The simulation study shows that the proposed method can substantially improve the estimation efficiency especially for the constant coefficients. A real-world example based on the Dublin voter turnout data is given to demonstrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

According to standard assumptions in semantics, (a) ordinary users of a language have implicit beliefs about the truth-conditions of sentences in that language, and (b) they often agree on those beliefs. For example, it is assumed that if Anna and John are both competent users of English and the former utters 'grass is green' in conversation with the latter, they will both believe that that sentence is true if and only if grass is green. These assumptions play an important role in an intuitively compelling picture of communication, according to which successful communication through literal assertoric utterances is normally effected thanks to our shared beliefs about the truth-conditions of the sentences uttered in the course of the conversation. Against these standard assumptions, this paper argues that the participants in a conversation rarely have the same beliefs about the truth-conditions of the sentences involved in a linguistic interaction. More precisely, it argues for Variance, the thesis that nearly every utterance is such that there is no proposition which more than one language user believes to be that utterance's truth-conditional content. If Variance is true, we must reject the standard picture of communication. Towards the end of the paper I identify three ways in which ordinary conversations can be communication-like despite the truth of Variance and argue that the most natural amendments to the standard picture fail to explain them. [ABSTRACT FROM AUTHOR]

When training a deep neural network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. We can divide latent features into (i) 'core' or 'conditionally invariant' features C whose distribution C | Y , conditional on the class Y, does not change substantially across domains and (ii) 'style' features S whose distribution S | Y can change substantially across domains. Examples for style features include position, rotation, image quality or brightness but also more complex ones like hair color, image quality or posture for images of persons. Our goal is to minimize a loss that is robust under changes in the distribution of these style features. In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable. We do assume that we can sometimes observe a typically discrete identifier or " ID variable". In some applications we know, for example, that two images show the same person, and ID then refers to the identity of the person. The proposed method requires only a small fraction of images to have ID information. We group observations if they share the same class and identifier (Y , ID) = (y , id) and penalize the conditional variance of the prediction or the loss if we condition on (Y , ID) . Using a causal framework, this conditional variance regularization (CoRe) is shown to protect asymptotically against shifts in the distribution of the style variables in a partially linear structural equation model. Empirically, we show that the CoRe penalty improves predictive accuracy substantially in settings where domain changes occur in terms of image quality, brightness and color while we also look at more complex changes such as changes in movement and posture. [ABSTRACT FROM AUTHOR]