Residual Variance Research Articles

Language and Memory Reserve Mediate Protective Effects of Social Support on MCI or Dementia Risk in Older Women

More supportive social relationships are protective of cognitive decline in older adulthood. Although supportive social relationships are hypothesized to promote cognitive reserve (CR; the cognitive adaption to neuropathology), it is unknown whether CR mediates associations between social support and risk of developing mild cognitive impairment or dementia (MCI/dementia). Data from 815 women (aged 73-87 years) participating in the Women’s Health Initiative Memory Study-MRI cohort (WHIMS-MRI) and Women’s Health Initiative Study of Cognitive Aging (WHISCA) were analyzed to examine whether domain-specific estimates of CR mediate associations between social support and incident MCI/dementia risk. Women completed the Medical Outcomes Study Social Support Scale (MOS-SS) in 2004-2005, a structural MRI (sMRI) of the brain in 2005-06, and annual extensive neuropsychological examinations till 2018. CR (6-months after completing the MOS-SS) was estimated across different domains (e.g. verbal memory, figural memory, language, visuospatial, and attention) as the residual variance after regressing out effects of sMRI variables, sociodemographic factors, and measurement error. Structural equation models were constructed to examine whether CR mediate associations between social support and MCI/dementia risk while adjusting for covariates. Higher social support was associated with lower MCI/dementia risk (hazard ratio=0.85 per 1-SD;p=0.037), higher language reserve (standardized β=0.09;p=0.008) and verbal memory reserve (standardized β=0.08;p=0.025). Language and verbal memory reserve each significantly explained approximately 14% of the protective effect of social support. Findings illustrate the heterogeneous effect of social support on CR, highlighting the importance of language and verbal memory reserve as mediators of the association between social support and MCI/dementia risk.

Using Random Regression Models to Genetically Evaluate Functional Longevity Traits in North American Angus Cattle.

Simple SummaryCattle longevity is usually defined as the duration of life of a cow from first calving to death. In addition to a longer lifespan, it is crucial that cows are productive throughout their lives. Incorporating optimal indicators of productive longevity in breeding schemes will directly improve the economic profitability of the beef cattle herd and long-term sustainability of the industry. Thus, the impact of different longevity indicators in the selection of North American Angus cattle was evaluated and optimal parameters were defined to perform the evaluations.This study aimed to propose novel longevity indicators by comparing genetic parameters for traditional (TL; i.e., the cow’s lifespan after the first calving) and functional (FL; i.e., how long the cow stayed in the herd while also calving; assuming no missing (FLa) or missing (FLb) records for unknown calving) longevity, considering different culling reasons (natural death, structural problems, disease, fertility, performance, and miscellaneous). Longevity definitions were evaluated from 2 to 15 years of age, using single- and multiple-trait Bayesian random regression models (RRM). The RRM fitting heterogenous residual variance and fourth order Legendre polynomials were considered as the optimal models for the majority of longevity indicators. The average heritability estimates over ages for FLb (from 0.08 to 0.25) were always higher than those for FLa (from 0.07 to 0.19), and higher or equal to the ones estimated for TL (from 0.07 to 0.23), considering the different culling reasons. The average genetic correlations estimated between ages were low to moderate (~0.40), for all longevity definitions and culling reasons. However, removing the extreme ages (i.e., 2 and >12 years) increased the average correlation between ages (from ~0.40 to >0.70). The genetic correlations estimated between culling reasons were low (0.12 and 0.20 on average, considering all ages and ages between 3 and 12 years old, respectively), indicating that longevity based on different culling reasons should be considered as different traits in the genetic evaluations. Higher average genetic correlations (estimated from 3 to 12 years old) were observed between TL and FLb (0.73) in comparison to TL and FLa (0.64), or FLa and FLb (0.65). Consequently, a higher average proportion of commonly-selected sires, for the top 1% sires, was also observed between TL and FLb (91.74%), compared to TL and FLa (59.68%), or FLa and FLb (61.01%). Higher prediction accuracies for the expected daughter performances (calculated based on the pedigree information) were obtained for FLb in comparison to TL and FLa. Our findings indicate that FLb is preferred for the genetic evaluation of longevity. In addition, it is recommended including multiple longevity traits based on different groups of culling reasons in a selection sub-index, as they are genetically-different traits. Genetic selection based on breeding values at the age of four years is expected to result in greater selection responses for increased longevity in North American Angus cattle.

Genetic analysis of resilience indicators based on milk yield records in different lactations and at different lactation stages

Resilience is the ability of cows to cope with disturbances, such as pathogens or heat waves. To breed for improved resilience, it is important to know whether resilience genetically changes throughout life. Therefore, the aim was to perform a genetic analysis on 2 resilience indicators based on data from 3 periods of the first lactation (d 11-110, 111-210, and 211-340) and the first 3 full lactations, and to estimate genetic correlations with health traits. The resilience indicators were the natural log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily deviations in milk yield from an expected lactation curve. Low LnVar and rauto indicate low variability in daily milk yield and quick recovery, and were expected to indicate good resilience. Data of 200,084 first, 155,784 second, and 89,990 third lactations were used. Heritabilities were similar based on different lactation periods (0.12-0.15 for LnVar, 0.05-0.06 for rauto). However, the heritabilities of the resilience indicators based on full first lactation were higher than those based on lactation periods (0.20 for LnVar, 0.08 for rauto), due to lower residual variances. Heritabilities decreased from 0.20 in full lactation 1 to 0.19 in full lactation 3 for LnVar and from 0.08 to 0.06 for rauto. For LnVar, as well as for rauto, the strongest genetic correlation between lactation periods was between period 2 and 3 (0.97 for LnVar, 0.96 for rauto) and the weakest between period 1 and 3 (0.81 for LnVar, 0.65 for rauto). Similarly, for both traits the genetic correlation between full lactations was strongest between lactations 2 and 3 (0.99 for LnVar, 0.95 for rauto) and weakest between lactations 1 and 3 (0.91 for LnVar, 0.71 for rauto). For LnVar, genetic correlations with resilience-related traits, such as udder health, ketosis, and longevity, adjusted for correlations with milk yield, were almost always favorable (-0.59 to 0.02). In most cases these genetic correlations were stronger based on full lactations than on lactation periods. Genetic correlations were similar across full lactations, but the correlation with udder health increased substantially from -0.31 in lactation 1 to -0.51 in lactation 3. For rauto, genetic correlations with resilience-related traits were always favorable in lactation period 1 and in most full lactations, but not in the other lactation periods. However, correlations were weak (-0.27 to 0.15). Therefore, as a resilience indicator for breeding, LnVar is preferred over rauto. A multitrait index based on estimated breeding values for LnVar in lactations 1, 2, and 3 is recommended to improve resilience throughout the lifetime of a cow.

Estimation of Uncertainty in Airborne LiDAR Inventories Using Approaches Based on Bootstrapping-Pairs Methods

LiDAR inventories were carried out to estimate the mean volume and variance in Eucalyptus globulus and Eucalyptus nitens stands. Uncertainty of the population estimates was examined using approximations based on the bootstrap method. Three methods were tested, the traditional bootstrapping-pair method (Method 1) and two additional methods in which the residual variance of the models was incorporated. Method 2 incorporated the residual variance in homoscedastic structure and Method 3 incorporated the heteroscedastic residual variance. Bootstrapping-pairs based on Method 3 generated similar estimates for the mean volume and slightly higher estimates for the variance as the traditional method (Method 1). Variance estimates obtained with the traditional bootstrapping-pairs method could be biased due to the presence of heteroscedasticity. Method 3 was found to best estimate the variance of the mean volume in LiDAR inventories, when the models that describe the relationship between stand variables and LiDAR metrics do not meet the assumption of homoscedasticity. It is shown that the uncertainty of the estimation of the average volume decreased in stands with a larger area, stabilizing the uncertainty of estimates in stands with areas larger than 50 hectares. Our results suggest that the residual variance in the heteroscedastic structure must be incorporated to avoid bias when bootstrapping-pairs are used in small area stands (less than 5 hectares).

Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population.

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

Adaptability and stability analyses of plants using random regression models.

The evaluation of cultivars using multi-environment trials (MET) is an important step in plant breeding programs. One of the objectives of these evaluations is to understand the genotype by environment interaction (GEI). A method of determining the effect of GEI on the performance of cultivars is based on studies of adaptability and stability. Initial studies were based on linear regression; however, these methodologies have limitations, mainly in trials with genetic or statistical unbalanced, heterogeneity of residual variances, and genetic covariance. An alternative would be the use of random regression models (RRM), in which the behavior of the genotypes is characterized as a reaction norm using longitudinal data or repeated measurements and information regarding a covariance function. The objective of this work was the application of RRM in the study of the behavior of common bean cultivars using a MET, based on Legendre polynomials and genotype-ideotype distances. We used a set of 13 trials, which were classified as unfavorable or favorable environments. The results revealed that RRM enables the prediction of the genotypic values of cultivars in environments where they were not evaluated with high accuracy values, thereby circumventing the unbalanced of the experiments. From these values, it was possible to measure the genotypic adaptability according to ideotypes, according to their reaction norms. In addition, the stability of the cultivars can be interpreted as variation in the behavior of the ideotype. The use of ideotypes based on real data allowed a better comparison of the performance of cultivars across environments. The use of RRM in plant breeding is a good alternative to understand the behavior of cultivars in a MET, especially when we want to quantify the adaptability and stability of genotypes.

Sympathetic nervous system dominance during stress recovery mediates associations between stress sensitivity and social anxiety symptoms in female adolescents.

Social anxiety disorder (SAD) is commonly diagnosed during adolescence and is associated with psychological stress reactivity and heightened physiological arousal. No study, however, has systematically examined which aspects of autonomic nervous system function mediate likely links between stress sensitivity and social anxiety symptoms in adolescents. Here, we assessed 163 adolescents (90 females; 12.29 ± 1.39 years) with respect to life stress and social anxiety symptoms, and measured respiratory sinus arrhythmia (RSA) and skin conductance levels (SCL) during a psychosocial stress paradigm. We operationalized stress sensitivity as the residual variance in subjective stress severity after accounting for objective severity and changes in autonomic regulation using standardized change scores in RSA and SCL. In females only, stress sensitivity and social anxiety symptoms were significantly correlated with each other (p < .001) and with autonomic regulation during both reactivity and recovery (all ps < 0.04). Further, sympathetic nervous system dominance during recovery specifically mediated associations between stress sensitivity and social anxiety symptoms (B = 1.06, 95% CI: 0.02-2.64). In contrast, in males, stress sensitivity, autonomic regulation during reactivity or recovery, and social anxiety symptoms were not significantly associated (all ps > 0.1). We interpret these results in the context of psychobiological models of SAD and discuss implications for interventions targeting autonomic processes.

FITTING AND CALIBRATING A MIXED-EFFECTS SEGMENTED TAPER MODEL FOR BRUTIAN PINE

Taper models are one of several necessary tools in modern forest inventory, giving information on diameter at any point along the tree stem and this information can also be used to estimate stem volume. In this study, we used nonlinear mixed-effects (NLME) modeling approach to minimize existing statistical problems in constructing taper equations. A segmented taper model of Max and Burkhart (1976) was fitted using this approach to consider for within- and between-tree variation in brutian pine (Pinus brutia Ten.) stem taper. Based on evaluation statistics, the model including random-effects parameters β 1, β 3 and β 4 were found to be the best. Inclusion of random parameters were not completely eliminated heterogenous variance and autocorrelation in residuals. Incorporating variance function and a continuous autoregressive error structure (CAR(1)) to NLME Max and Burkhart model removed the heteroscedasticity and autocorrelation in residuals. Upper stem diameters were used to localized stem taper model to individual tree. For this, two different measurement scenarios were evaluated as one and two upper stem diameter measurements. Inclusion of random parameters were improved the predictive capability of taper model in particularly the middle and lower sections of stem based on upper stem diameter measurements. The calibration using upper stem diameter measurements can improve the tree-level accuracy of stem taper model is therefore recommended.

Variability in working memory performance on mobile devices is sensitive to mild cognitive impairment: Results from the Einstein Aging Study

AbstractBackgroundMost prior research on distinguishing cognitive impairment from healthy controls focuses on measuring mean‐level of cognitive performance on one or relatively few assessments, ignoring potentially valuable information about variability. Within‐person variability in cognitive performance is emerging as a promising indicator of mild cognitive impairment (MCI) and dementia. The limited research on this topic, however, typically examines speeded responses and variability transpiring across moments during a single test administration. The current study extends prior work by using ecological momentary assessments (EMA) to examine cognitive status differences in variability across hours and days in three dimensions of working memory (WM): object‐location binding, object‐feature binding, and spatial WM.MethodIn a sample of 243 systematically recruited, community‐dwelling adults age 70+ from the Einstein Aging Study (Mean age=77.46 years, SD=5.13), we identified 68 individuals meeting Jak‐Bondi criteria for MCI and 175 cognitively normal controls (Table 1). All participants performed object‐location binding, object‐feature binding, and spatial WM tasks on a smartphone up to four times daily for 14 days, totaling up to 56 assessments per person. We employed heterogeneous variance models using log‐linear prediction of residual variance to simultaneously assess MCI differences in mean performance and within‐person variability. Models adjusted for linear trends across assessments and study days, as well as age, education, gender, and ethnicity (Table 2; Figure 1).ResultsFor mean performance, individuals with MCI exhibited worse performance than healthy controls in measures of object‐location binding (Est.=0.14, SE=0.02, p&lt;.001), object‐feature binding (Est.=‐0.27, SE=0.04, p&lt;.001), and spatial WM (Est.=0.90, SE=0.20, p&lt;.001). For variability across assessments, individuals with MCI exhibited greater within‐person variability than controls in object‐location binding (Est.=0.89, SE=0.03, p&lt;.001) and object‐feature binding (Est.=0.37, SE=0.03, p&lt;.001) performance. Variability in spatial WM performance did not vary as a function of cognitive status (Est.=0.02, SE=0.03, p=.63).ConclusionResults suggest both mean and variability in performance are sensitive to MCI and exhibit dissociative patterns by WM dimension. Variability in object‐location and object‐feature binding performance may provide specific detection of MCI. The 14‐day smartphone‐based EMA offers novel opportunity to leverage performance variability across frequent assessments for identifying early clinical manifestations of cognitive impairment.

ADAPTIVE GUARANTEED ESTIMATION OF A CONSTANT SIGNAL UNDER UNCERTAINTY OF MEASUREMENT ERRORS

In the guaranteed estimation problems under uncertainty relative to disturbances and meas-urement errors, the admissible sets of their possible values are determined. The solution is chosen due to conditions of guaranteed bounded estimates optimization corresponding to the worst realiza-tion of disturbances and measurement errors. The result of the guaranteed estimation is an unim-provable bounded estimate (information set), which turns to be overly pessimistic (reinsurance) if a prior admissible set of measurement errors is too large compared to their realized values. The admis-sible sets of disturbances and measurement errors can turn to be only rough upper estimates on a short observation interval. The goal of research is the accuracy enhancement problem of guaran-teed estimation when measurement errors are not realized in the worst way, i.e. the environment in which the object operates does not behave as aggressively as it is built in a priori data on the permis-sible set of error values. Research design. The problem of adaptive guaranteed estimation of a con-stant signal from noisy measurements is considered. The adaptive filtering problem is, according to the results of measurement processing, from the whole set of possible realizations of errors, to choose the one that would generate the measurement sequence. Results. An adaptive guaranteed estimation algorithm is presented. The adaptive algorithm construction is based on a multi-alternative method based on the Kalman filter bank. The method uses a set of filters, each of which is tuned to a specific hypoth-esis about the measurement error model. Filter residuals are used to compute estimates of realized measurement errors. The choice of the realization of possible errors is performed using a function that has the meaning of the residual variance over a short time interval. Conclusion. The computa-tional scheme of the adaptive algorithm, the numerical example, and comparative analysis of ob-tained estimates are presented.

6 Feed intake variability and performance implications of adjusting for dry matter content of cattle feedlot feed ingredients exposed to the elements

Abstract Objectives of this study were: 1) validate on-farm dry matter (DM) testing methods, and 2) determine how as-fed dietary composition adjustment to respond to DM content changes affected performance. One-hundred fifty-one crossbred steers (n = 85; 404 kg initial BW) and heifers (n = 66; 384 kg initial BW) were allocated randomly (4 to 6 hd/pen), within truck load and sex, to one of two 15-pen rows in a deep-bedded confinement barn. Treatments included control (n = 15 pens), where diets were only modified to account for changes in ingredient DM every 28 d or adjusted (ADJ; n = 15 pens), in which diets were modified daily to account for changes in ingredient DM. Target composition of diets was (DM basis) hay (3.73%), corn silage (CS; 8.28%), high moisture corn (HMC; 50.86%), dry distillers grains with solubles (DDGS; 11.83%), dry rolled corn (DRC; 20.07%), and a liquid supplement (5.23%). Cattle were fed for 84 d. Dry matter content of hay was determined daily using a microwave oven and that of CS and HMC were determined using a forced-air dryer every other day upon removal from bunker silos; these methods were validated against DM determination using a forced-air oven set at 105 °C. Hay or corn silage DM measured indirectly was similar (P &amp;gt; 0.10) to that measured using a forced-air oven at 105 °C. Adjusting as-fed dietary composition daily to compensate for changes in ingredient DM had no effect on DMI, ADG, feed conversion or final BW. Within-pen feed delivery residual variance tended to be greater (P = 0.06) for pens managed under ADJ. Within-pen and between-treatment residual DMI variance was not different (P &amp;gt; 0.10). Under the conditions of this study, exposure to the elements did not precipitate in large changes in diet DM composition or content. This resulted in similar performance response whether dietary as-fed composition was adjusted or not.

95 Evaluation of variation attributable to lab and technician for measurements of beef carcass traits made using ultrasound

Abstract Carcass data that is collected using ultrasound contributes to national genetic evaluation of beef cattle. Cattle are scanned by many technicians and the resulting images are processed by three laboratories providing data to breed associations. The Ultrasound Guidelines Council certifies technicians and laboratories as being proficient. However, there has been no comprehensive evaluation of non-genetic sources of variation that affect the data. The objective of this study was to partition the variance of ultrasound carcass measurements to animal genetic effects, ultrasound scanning technician and image interpretation laboratory. Ultrasound carcass data for longissimus muscle area, percent intramuscular fat, and subcutaneous fat depth were provided by the American Angus Association (n=65,971), American Hereford Association (n=43,380), and American Simmental Association (n=48,298). For each breed variance components were estimated for additive genetic effects of each animal, technician, contemporary group nested within technician and residual. Genetic correlations were estimated treating measurements from the different interpretation laboratories as separate traits. Across all three breeds, additive genetic effects explained between 15 and 30%, 11 and 25%, and 6 and 13% of the phenotypic variance for IMF, SQF, and LMA, respectively. Similarly, technician explained 12-27%, 4-28%, and 4-26% of the phenotypic variance; contemporary groups within technician accounted for 25-45%, 20-54%, and 43-73% of phenotypic variance. Among the image interpretation laboratories, the ratio of greatest to least residual variance was 3.96, 1.62, and 1.50 for IMF, SQF, and LMA, respectively. Genetic correlations between labs ranged from 0.79 to 0.95 for IMF, 0.64 to 0.94 for SQF and 0.78 to 0.98 for LMA. These results suggest the potential for the residual variances to be heterogeneous, particularly for IMF, depending on the image interpretation laboratory.

A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification in marine and coastal ecosystems

Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators.

Multiple imputation and direct estimation for qPCR data with non-detects

BackgroundQuantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference.ResultsWe propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package nondetects.ConclusionsThe statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors.

Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well‐being. Many studies of neurocognitive aging have investigated brain function with the blood‐oxygen level‐dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath‐holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age‐related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age‐related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population‐based, lifespan Cam‐CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel‐level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

Testing for Factor Loading Differences in Mixture Simultaneous Factor Analysis: A Monte Carlo Simulation-Based Perspective

ABSTRACT Factor analysis is ubiquitously applied in behavioral sciences for capturing covariances of observed variables by latent variables (factors). When factor-analyzing data from many groups of subjects, mixture simultaneous factor analysis (MSFA) determines which groups have the same factor model by clustering them based on their factor loadings, factor (co)variances and residual variances. Two Monte Carlo simulations are performed to investigate the power and type I error of Wald tests for factor loading differences in MSFA, as affected by characteristics of the data (sample size in terms of number and size of groups), factor models (item communality levels, sizes and types of loading differences) and clustering (cluster size, classification error and uncertainty). The results were better in case of equal cluster sizes, strongly overdetermined factors, high communalities, and larger primary loading differences.

Adaptable Shack-Hartmann wavefront sensor with diffractive lenslet arrays to mitigate the effects of scintillation.

Adaptive optics systems are used to compensate for distortions of the wavefront of light induced by turbulence in the atmosphere. Shack-Hartmann wavefront sensors are used to measure this wavefront distortion before correction. However, in turbulence conditions where strong scintillation (intensity fluctuation) is present, these sensors show considerably worse performance. This is partly because the lenslet arrays of the sensor are designed without regard to scintillation and are not adaptable to changes in turbulence strength. Therefore, we have developed an adaptable Shack-Hartmann wavefront sensor that can flexibly exchange its lenslet array by relying on diffractive lenses displayed on a spatial light modulator instead of utilizing a physical microlens array. This paper presents the principle of the sensor, the design of a deterministic turbulence simulation test-bed, and an analysis how different lenslet arrays perform in scintillation conditions. Our experiments with different turbulence conditions showed that it is advantageous to increase the lenslet size when scintillation is present. The residual phase variance for an array with 24 lenslets was up to 71% lower than for a 112 lenslet array. This shows that the measurement error of focal spots has a strong influence on the performance of a Shack-Hartmann wavefront sensor and that in many cases it makes sense to increase the lenslet size. With our adaptable wavefront sensor such changes in lenslet configurations can be done very quickly and flexibly.

Interrupting Prolonged Sitting Reduces Postprandial Glucose Concentration in Young Men With Central Obesity.

Prolonged sitting elevates postprandial metabolic markers, resulting in increased risks of cardiovascular diseases and type 2 diabetes. Interrupting prolonged sitting may reduce these risks. However, more information is needed to understand the patterns of interrupting prolonged sitting to obtain metabolic health benefits. This study examined the effects of interrupting prolonged sitting with different intensities and durations of walking with an equivalent energy expenditure on postprandial metabolic responses in young Chinese men with central obesity. A randomized crossover experimental trial was conducted. Participants underwent three 6-hour experiments with a 7-day washout period between each experiment: prolonged sitting, 3 min of light-intensity walking every 30 minutes, and 1.5 minutes of moderate-intensity walking every 30 minutes. Baseline (fasting) and 6-hour postprandial metabolic glucose and lipid levels were analyzed among 18 young Chinese men with central obesity. Generalized estimating equations (adjusted for the potential confounders explaining residual outcome variance (body mass index) and age), trial order, preprandial values, and lead-in activity) were used, and the incremental areas under the curve (iAUC) of each outcome were compared between prolonged sitting and interrupted prolonged sitting conditions. Compared with prolonged sitting, both interrupting prolonged sitting conditions reduced the iAUCs for glucose (P < .05) but not insulin, C-peptide, triglycerides, or nonesterified fatty acids. Both conditions of interrupted prolonged sitting reduced postprandial glucose concentrations in young Chinese men with central obesity when the energy expenditure was equivalent.

Variance constraints strongly influenced model performance in growth mixture modeling: a simulation and empirical study

BackgroundGrowth Mixture Modeling (GMM) is commonly used to group individuals on their development over time, but convergence issues and impossible values are common. This can result in unreliable model estimates. Constraining variance parameters across classes or over time can solve these issues, but can also seriously bias estimates if variances differ. We aimed to determine which variance parameters can best be constrained in Growth Mixture Modeling.MethodsTo identify the variance constraints that lead to the best performance for different sample sizes, we conducted a simulation study and next verified our results with the TRacking Adolescent Individuals’ Lives Survey (TRAILS) cohort.ResultsIf variance parameters differed across classes and over time, fitting a model without constraints led to the best results. No constrained model consistently performed well. However, the model that constrained the random effect variance and residual variances across classes consistently performed very poorly. For a small sample size (N = 100) all models showed issues. In TRAILS, the same model showed substantially different results from the other models and performed poorly in terms of model fit.ConclusionsIf possible, a Growth Mixture Model should be fit without any constraints on variance parameters. If not, we recommend to try different variance specifications and to not solely rely on the default model, which constrains random effect variances and residual variances across classes. The variance structure must always be reported Researchers should carefully follow the GRoLTS-Checklist when analyzing and reporting trajectory analyses.

Genetic and nongenetic factors associated with lactation length in seasonal-calving, pasture-based dairy cows

Lactation yield estimates standardized to common lactation lengths of 270-d or 305-d equivalents are commonly used in management decision support tools and dairy cow genetic evaluations. The use of such measurements to quantify the (genetic) merit of individual cows fails to penalize cows that do not reach the standardized lactation length, or indeed reward cows that lactate for more than the standardized lactation length. The objective of the present study was to quantify the genetic and nongenetic factors associated with lactation length in seasonal-calving, pasture-based dairy cows. A total of 616,350 lactation length records from 285,598 Irish cows were used. Linear mixed models were used to quantify the associations between lactation length and calving month, parity, age at calving, previous dry period length, calving difficulty score, heterosis, recombination loss, breed, and herd size, as well as to estimate the genetic and residual variance components of lactation length. The median lactation length in the edited data set was 288 d, with 27% of cows achieving lactations of at least 305 d. Relative to cows calving in January, the lactations of cow calving in February, March, or April was, on average, 4.2, 12.7, and 21.9 d shorter, respectively. The lactation length of a first parity cow was, on average, 7.8, 8.6, and 8.4 d shorter than that of second, third, and fourth parity cows, respectively. Norwegian Red and Montbéliarde cows had, on average, a 4.7- and 1.6-d shorter lactation than Holstein-Friesian cows, respectively. The heritability estimate, coefficient of genetic variation, and repeatability estimate of lactation length were 0.02, 1.2%, and 0.04, respectively. Based on the genetic standard deviation for lactation length estimated in the present study (3.3 d), cows ranked in the top 20% for genetic merit for lactation length would be expected to have lactations 9.2 d longer than cows in the bottom 20%, demonstrating exploitable genetic variability. Given the vast array of genetic and nongenetic factors associated with lactation length, an approach which combines improved management practices and selective breeding may be an efficient and effective strategy to lengthen lactations.