Variance Heterogeneity Research Articles

Detecting High-Dose Methotrexate-Induced Brain Changes in Pediatric and Young Adult Cancer Survivors Using [18F]FDG PET/MRI: A Pilot Study.

Significant improvements in treatments for children with cancer have resulted in a growing population of childhood cancer survivors who may face long-term adverse outcomes. Here, we aimed to diagnose high-dose methotrexate-induced brain injury on [18F]FDG PET/MRI and correlate the results with cognitive impairment identified by neurocognitive testing in pediatric cancer survivors. Methods: In this prospective, single-center pilot study, 10 children and young adults with sarcoma (n = 5), lymphoma (n = 4), or leukemia (n = 1) underwent dedicated brain [18F]FDG PET/MRI and a 2-h expert neuropsychologic evaluation on the same day, including the Wechsler Abbreviated Scale of Intelligence, second edition, for intellectual functioning; Delis-Kaplan Executive Function System (DKEFS) for executive functioning; and Wide Range Assessment of Memory and Learning, second edition (WRAML), for verbal and visual memory. Using PMOD software, we measured the SUVmean, cortical thickness, mean cerebral blood flow (CBFmean), and mean apparent diffusion coefficient of 3 different cortical regions (prefrontal cortex, cingulate gyrus, and hippocampus) that are routinely involved during the above-specified neurocognitive testing. Standardized scores of different measures were converted to z scores. Pairs of multivariable regression models (one for z scores < 0 and one for z scores > 0) were fitted for each brain region, imaging measure, and test score. Heteroscedasticity regression models were used to account for heterogeneity in variances between brain regions and to adjust for clustering within patients. Results: The regression analysis showed a significant correlation between the SUVmean of the prefrontal cortex and cingulum and DKEFS-sequential tracking (DKEFS-TM4) z scores (P = 0.003 and P = 0.012, respectively). The SUVmean of the hippocampus did not correlate with DKEFS-TM4 z scores (P = 0.111). The SUVmean for any evaluated brain regions did not correlate significantly with WRAML-visual memory (WRAML-VIS) z scores. CBFmean showed a positive correlation with SUVmean (r = 0.56, P = 0.01). The CBFmean of the cingulum, hippocampus, and prefrontal cortex correlated significantly with DKEFS-TM4 (all P < 0.001). In addition, the hippocampal CBFmean correlated significantly with negative WRAML-VIS z scores (P = 0.003). Conclusion: High-dose methotrexate-induced brain injury can manifest as a reduction in glucose metabolism and blood flow in specific brain areas, which can be detected with [18F]FDG PET/MRI. The SUVmean and CBFmean of the prefrontal cortex and cingulum can serve as quantitative measures for detecting executive functioning problems. Hippocampal CBFmean could also be useful for monitoring memory problems.

The association between restricted activity and patient outcomes in older adults: systematic literature review and meta-analysis

BackgroundRestricted activity is a potential early marker of declining health in older adults. Previous studies of this association with patient outcomes have been inconclusive. This review aimed to evaluate the extent to which restricted activity is associated with decline in health.MethodsA search was conducted for studies including people over 65 years old which investigated the association between measures of restricted activity and hospitalisation, cognitive decline, and mortality. Following data extraction by two reviewers, eligible studies were summarised using Inverse Variance Heterogeneity meta-analysis.ResultsThe search identified 8,434 unique publications, with 11 eligible studies. Three measures of restricted activity were identified: bed rest, restricted movement, and dependency for activities of daily living (ADL). Three studies looked at hospitalisations, with two finding a significant association with bed rest or restricted movement and one showing no evidence of an association. Restricted activity was associated with a significant increase in mortality across all three measures (bed rest odds ratio [OR] 6.34, 95%CI 2.51–16.02, I2 = 76%; restricted movement OR 5.38 95%CI 2.60–11.13, I2 = 69%; general ADL dependency OR 4.65 95%CI 2.25–9.26, I2 = 84%). The significant heterogeneity observed could not be explained by restricting the analysis by length of follow-up, or measure of restricted activity. No meta-analysis was conducted on the limited evidence for cognitive decline outcomes.ConclusionsLimited studies have considered the prognostic value of restricted activity in terms of predicting future declining health. Current evidence suggests restricted activity is associated with hospitalisation and mortality, and therefore could identify a group for whom early intervention might be possible.

Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances

We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between firm-level research and development (R&D) investments and the business cycle. We find a well-defined group structure in the variances that ex-post can be related to firm size. Our estimates indicate stronger procyclical investment patterns at medium-size firms compared to large firms.

Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization

In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. The performance of our proposed method is demonstrated by the extensive numerical and case studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.

Transanal irrigation in children with functional constipation: A systematic review and meta-analysis.

Refractory functional constipation is a challenging condition to manage in children. The use of transanal irrigation (TAI) is well reported in children with neurological disorders as well as anorectal malformations but less so in children with functional disorders of defecation. The objective of our study was to evaluate the effectiveness, safety and outcomes of TAI in children with functional constipation. PubMed, Scopus and Google Scholar were searched for publications related to the use of TAI in functional constipation. Data regarding the study design, sample size, patient characteristics, investigator-reported response to TAI and adverse effects were extracted from studies that met the selection criteria. The inverse variance heterogeneity model was used for ascertaining the summary effect in this meta-analysis. The search strategy yielded 279 articles of which five studies were included in the final review. The studies were from the United Kingdom (n = 2), Netherlands (n = 2) and Denmark (n = 1). These studies included 192 children with a median age ranging from 7 to 12.2 years old. The TAI systems used in these studies were: Peristeen (n = 2), Peristeen or Qufora (n = 1), Alterna (n = 1) and Navina (n = 1). The follow-up duration ranged from 5.5 months to 3 years. Eleven (5.7%) children did not tolerate TAI and withdrew from treatment soon after initiation. The pooled investigator-reported success of TAI was 62% (95% CI: 52%-71%). The most common adverse event was pain which was experienced by 21.7% of children. A total of 27 (14%) were successfully weaned off TAI at the last follow-up. TAI is reported to be successful in 62% of children with refractory functional constipation. There is a need for well-designed prospective trials to evaluate this treatment option in children with refractory functional constipation.

An investigation of heterogeneous impact, temporal stability, and aggregate shift in factors affecting the driver injury severity in single-vehicle rollover crashes

Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years’ models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity.

Analysis of Factors Affecting the Severity of Injuries in Electric Two-Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances

Electric two-wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two-wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two-wheeled vehicle riders were considered separately analyzed based on the data of 6403 two-wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP-HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two-wheelers, multinomial logit (MN-logit model), and random parameter logit model (RP-logit) were estimated, and the results showed that the RP-HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were &gt;60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility &lt;200 m, where the severe weather and visibility &lt;200 m were random parameters obeying normal distributions, there is a significant difference between having street lights and no control at night in both scenarios, and the difference is significant. The results of the study can provide a reference for the development of targeted countermeasures to improve the traffic safety of electric two-wheeled vehicles in China.

An error components mixed logit with heterogeneity in means and variance for fixed object occupant severity outcomes

This paper presents an error components mixed logit with heterogeneity in means and variance to capture the heterogeneous effects of contributing factors on fixed object occupant severity. One year (2021) of crash data on fixed object related crashes in Lubbock County, Texas was analyzed with fixed object details extracted from crash narratives and classified into 11 groupings. Crash data included any fixed object collision occurring at any point in the sequence of crash events (not exclusive to the first harmful event). The random parameters were identified as indicators for occupant involvement in the first harmful crash sequence event, with that event being collision with a fixed object, for possible injury and injury severity outcomes. Heterogeneity in the means of these random parameters was found with respect to six different indicator variables. Additionally, heterogeneity in the variance of the injury random parameter was found with respect to two different indicator variables. Inclusion of two error component nests improved prediction accuracy at the observation level for higher severity outcomes. The findings in this study suggest that fixed object classification types should be explored further in relation to heterogeneous effects on occupant severity outcomes. Furthermore, the findings also highlight the applicability of an error components mixed logit model for severity analysis.

Estimation of heterogeneous population variance using memory-type estimators based on EWMA statistic in the presence of measurement error for time-scaled surveys

In this present article, we have suggested memory-type ratio, exponential ratio, product and exponential product estimators based on exponentially weighted moving average statistic for the estimation of heterogeneous population variance using stratified sampling design in presence of measurement error for time-scaled surveys. Mathematical expressions of approximate mean square error are derived using Taylor and exponential expansions for the proposed memory-type estimators. We have also discussed the situations in which the memory-type estimators would perform efficiently than the conventional estimators. The results of extensive simulation study revealed that the proposed memory-type estimators based exponentially weighted moving average statistic would perform better than the conventional estimators in the presence measurement error for time-scaled surveys under certain condition.

Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS). Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan's socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests. Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES. Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.

Comparison of bias adjustment in meta-analysis using data-based and opinion-based methods.

Several methods exist for bias adjustment of meta-analysis results, but there has been no comprehensive comparison with unadjusted methods. We compare 6 bias-adjustment methods with 2 unadjusted methods to examine how these different methods perform. We re-analyzed a meta-analysis that included 10 randomized controlled trials. Two data-based methods (Welton's data-based approach and Doi's quality effects model) and 4 opinion-informed methods (opinion-based approach, opinion-based distributions combined statistically with data-based distributions, numerical opinions informed by data-based distributions, and opinions obtained by selecting areas from data-based distributions) were used to incorporate methodological quality information into the meta-analytical estimates. The results of these 6 methods were compared with 2 unadjusted models: the DerSimonian-Laird random effects model and Doi's inverse variance heterogeneity model. The 4 opinion-based methods returned the random effects model estimates with wider uncertainty. The data-based and quality effects methods returned different results and aligned with the inverse variance heterogeneity method with some minor downward bias adjustment. Opinion-based methods seem to only add uncertainty rather than bias adjust.

Microenvironmental sensitivity of body weight at harvest in Nile tilapia: Genetic parameters, performance test traits, and genetic variability

Uniformity of body weight at harvest is a trait of great economic importance in Nile tilapia production. Studying the genetic aspects of microenvironment sensitivity proves to be a valuable instrument for enhancing the uniformity of production. The microenvironmental sensitivity of a given genotype can be quantified by analyzing the degree of genetic heterogeneity of environmental variance. This study involved the analysis of a database comprising phenotypic records of body weight at harvest for 10,207 individuals. It aimed to estimate genetic parameters, scrutinize the effects of direct selection, and evaluate how selection impacts the genetic variability within a Nile tilapia breeding program. Estimates of genetic parameters were obtained by Bayesian procedures using a two-step animal model in GIBBS1F90 (BLUPF90 family) software. The coefficients of variation for residual variance (0.29) and genetic correlation (−0.43) between body weight at harvest and microenvironmental sensitivity were deemed moderately favorable. Direct selection for microenvironmental sensitivity resulted in a 41% increase in the coancestry coefficient. The group chosen for low microenvironmental sensitivity exhibited a significant difference of 19.08% in the coefficient of variation from the group selected for high microenvironmental sensitivity. No significant differences in the coefficient of variation were observed between tilapia of the commercial line selected for growth rate and those selected for low microenvironmental sensitivity. Selection for microenvironmental sensitivity has the potential to enhance production uniformity. Significantly, directional breeding for performance achieves a comparable improvement in uniformity. Nevertheless, opting for direct selection for microenvironmental sensitivity could result in a decrease in genetic variability within Nile tilapia breeding programs.

A bootstrap-based approach to combine individual-based forest growth models and remotely sensed data

Abstract Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.

Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis

IntroductionCognitive impairments present challenges for patients, impacting memory, attention, and problem-solving abilities. Virtual reality (VR) offers innovative ways to enhance cognitive function and well-being. This study explores the effects of VR-based training programs and games on improving cognitive disorders.MethodsPubMed, Scopus, and Web of Science were systematically searched until May 20, 2023. Two researchers selected and extracted data based on inclusion and exclusion criteria, resolving disagreements through consultation with two other authors. Inclusion criteria required studies of individuals with any cognitive disorder engaged in at least one VR-based training session, reporting cognitive impairment data via scales like the MMSE. Only English-published RCTs were considered, while exclusion criteria included materials not primarily focused on the intersection of VR and cognitive disorders. The risk of bias in the included studies was assessed using the MMAT tool. Publication bias was assessed using funnel plots and Egger’s test. The collected data were utilized to calculate the standardized mean differences (Hedges’s g) between the treatment and control groups. The heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was conducted using Stata version 17.0.ResultsTen studies were included in the analysis out of a total of 3,157 retrieved articles. VR had a statistically significant improvement in cognitive impairments among patients (Hedges’s g = 0.42, 95% CI: 0.15, 0.68; p_value = 0.05). games (Hedges’s g = 0.61, 95% CI: 0.30, 0.39; p_value = 0.20) had a more significant impact on cognitive impairment improvement compared to cognitive training programs (Hedges’s g = 0.29, 95% CI: -0.11, 0.69; p_value = 0.24). The type of VR intervention was a significant moderator of the heterogeneity between studies.ConclusionVR-based interventions have demonstrated promise in enhancing cognitive function and addressing cognitive impairment, highlighting their potential as valuable tools in improving care for individuals with cognitive disorders. The findings underscore the relevance of incorporating virtual reality into therapeutic approaches for cognitive disorders.

Randomized Controlled Trial of the Behavioral Intervention for Physical Activity in Multiple Sclerosis Project: Response Heterogeneity and Predictors of Change.

We reported that a social cognitive theory-based (SCT), Internet-delivered behavioral intervention increased device-measured minutes/day of moderate-to-vigorous physical activity (MVPA) over a 6-month period among persons with multiple sclerosis (MS). This paper examined the pattern and predictors of heterogeneity in change for MVPA. Based on previous research, we hypothesized that mild MS disability, fewer MS symptoms, lower baseline MVPA, and positive SCT characteristics (e.g., high exercise self-efficacy) would be associated with greater change in MVPA. Persons with MS (N = 318) were randomized into behavioral intervention (n = 159) or attention/social contact control (n = 159) conditions that were administered via Internet websites and supported with behavioral coaching. Demographic, clinical, symptom, behavioral, and SCT data were from before the 6-month period of delivering the conditions, and MVPA data were from before and after the 6-month period. We examined heterogeneity based on waterfall plots, box plots, and the Levene statistic. We identified predictors of MVPA change using bivariate correlation and multiple, linear regression analyses per condition. The Levene statistic indicated statistically significant heterogeneity of variances for MVPA change between conditions (p = .003), and the waterfall plots and box plots indicated greater heterogeneity in MVPA change for the behavioral intervention. MVPA change score was correlated with baseline MVPA (r = - .33 and r = - .34, p = .0004 and p = .0001) in both conditions and walking impairment (r = - .188, p = .047) and race (r = .233, p = .014) in the behavioral intervention condition. The regression analysis indicated that baseline MVPA (Standardized B = - .449, p = .000002), self-reported walking impairment (Standardized B = - .310, p = .0008), and race (Standardized B = .215, p = .012) explained 25.6% of variance in MVPA change for the behavioral intervention condition. We provide evidence for walking impairment, baseline MVPA, and race as predictors of the heterogeneity in the pattern of MVPA change with a behavioral intervention.

THE IMPLEMENTATION OF THE ALEXANDER-GOVERN TEST IN FACTORIAL DESIGN ANALYSIS

This study proposed to evaluate the performance of the Alexander-Govern test (AG test), Analysis of Variance (ANOVA), and t-test by analyzing the Type I error rate. The AG test is regarded as a reliablecontrol Type I error rate. This technique is insensitive in the presence of heteroscedasticity under a normal distribution. Simulation research was carried out using Statistical Analysis Software (SAS) to assess the effectiveness of the tests that are based on the rate of Type I error. By creating the conditions that could highlight the strengths and weaknesses of each test, three variables are being manipulated: sample size, variance heterogeneity, and type of pairings. The performance of the AG tests is convincing when it is able to control the Type I error rates better compared to ANOVA under all conditions of heterogeneous variances. Meanwhile, the ANOVA performs best only when the variances are homogenous. A real data experiment was applied to validate the result. In the battery life design experiment, the p-value using the AG test and ANOVA are computed and compared. The AG test provides valid results when it can test the main effect and the interaction effect, as well as the ANOVA. With good performance in the simulation study, the AG test can be considered a good alternativeto the ANOVA when the assumptions of the homogeneity of variances are violated in the case of factorial design.

Genotype-Environment Interaction in ADHD: Genetic Predisposition Determines the Extent to Which Environmental Influences Explain Variability in the Symptom Dimensions Hyperactivity and Inattention

Although earlier research has shown that individual differences on the spectrum of attention deficit hyperactivity disorder (ADHD) are highly heritable, emerging evidence suggests that symptoms are associated with complex interactions between genes and environmental influences. This study investigated whether a genetic predisposition [Note that the term ‘genetic predisposition’ was used in this manuscript to refer to an estimate based on twin modeling (an individual’s score on the latent trait that resembles additive genetic influences) in the particular population being examined.] for the symptom dimensions hyperactivity and inattention determines the extent to which unique-environmental influences explain variability in these symptoms. To this purpose, we analysed a sample drawn from the Twins Early Development Study (TEDS) that consisted of item-level scores of 2168 16-year-old twin pairs who completed both the Strengths and Difficulties Questionnaire (SDQ; Goodman, in J Child Psychol Psychiatry 38:581–586, 1997) and the Strength and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN; Swanson, in Paper presented at the meeting of the American Psychological Association, Los Angeles, 1981) questionnaire. To maximize the psychometric information to measure ADHD symptoms, psychometric analyses were performed to investigate whether the items from the two questionnaires could be combined to form two longer subscales. In the estimation of genotype-environment interaction, we corrected for error variance heterogeneity in the measurement of ADHD symptoms through the application of item response theory (IRT) measurement models. A positive interaction was found for both hyperactivity (e.g., β1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\beta }_{1}$$\\end{document} = 2.20 with 95% highest posterior density interval equal to [1.79;2.65] and effect size equal to 3.00) and inattention (e.g., β1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\beta }_{1}$$\\end{document} = 2.16 with 95% highest posterior density interval equal to [1.56;2.79] and effect size equal to 3.07). These results indicate that unique-environmental influences were more important in creating individual differences in both hyperactivity and inattention for twins with a genetic predisposition for these symptoms than for twins without such a predisposition.

Comparing the Performance of the Expected Returns of Cryptocurrencies Using CAPM and D-CAPM Approaches

The present study compares the expected returns of cryptocurrencies using the capital asset pricing and the downside capital asset pricing models. For this purpose, fifty cryptocurrencies were studied as representative of risky assets during the five-year from 2018 to 2022 with daily frequency. Using the conditional variance test, eighteen cryptocurrencies were accepted and the rest were homogeneously rejected in the variance heterogeneity test. Among the eighteen cryptocurrencies, nine were randomly selected as the portfolio, including high-volatility, low-volatility, and medium-volatility cryptocurrencies. First, using the t-Student statistic, the total return and downside return were compared. Results showed that the capital asset pricing model and the downside risk asset pricing model can be fitted in cryptocurrencies. Using the Wald-Fisher model, we investigated the justification for selecting a more appropriate model based on downside returns. The results of the research showed that D-CAPM and CAPM show the relationship between risk and return in cryptocurrencies appropriately, and the portfolios obtained from the mentioned models show the efficiency of the two models in the cryptocurrency market.

On the External Validity of Average-case Analyses of Graph Algorithms

The number one criticism of average-case analysis is that we do not actually know the probability distribution of real-world inputs. Thus, analyzing an algorithm on some random model has no implications for practical performance. At its core, this criticism doubts the existence of external validity ; i.e., it assumes that algorithmic behavior on the somewhat simple and clean models does not translate beyond the models to practical performance real-world input. With this article, we provide a first step toward studying the question of external validity systematically. To this end, we evaluate the performance of six graph algorithms on a collection of 2,740 sparse real-world networks depending on two properties: heterogeneity (variance in the degree distribution) and locality (tendency of edges to connect vertices that are already close). We compare this with the performance on generated networks with varying locality and heterogeneity. We find that the performance in the idealized setting of network models translates surprisingly well to real-world networks. Moreover, heterogeneity and locality appear to be the core properties impacting the performance of many graph algorithms.

Multienvironment genomic prediction in tetraploid potato.

Multienvironment genomic prediction was applied to tetraploid potato using 147 potato varieties, tested for 2 years, in 3 locations representative of 3 distinct regions in Europe. Different prediction scenarios were investigated to help breeders predict genotypic performance in the regions from one year to the next, for genotypes that were tested this year (scenario 1), as well as new genotypes (scenario 3). In scenario 2, we predicted new genotypes for any one of the 6 trials, using all the information that is available. The choice of prediction model required assessment of the variance-covariance matrix in a mixed model that takes into account heterogeneity of genetic variances and correlations. This was done for each analyzed trait (tuber weight, tuber length, and dry matter) where examples of both limited and higher degrees of heterogeneity was observed. This explains why dry matter did not need complex multienvironment modeling to combine environments and increase prediction ability, while prediction in tuber weight, improved only when models were flexible enough to capture the heterogeneous variances and covariances between environments. We also found that the prediction abilities in a target trial condition decreased, if trials with a low genetic correlation to the target were included when training the model. Genomic prediction in tetraploid potato can work once there is clarity about the prediction scenario, a suitable training set is created, and a multienvironment prediction model is chosen based on the patterns of G×E indicated by the genetic variances and covariances.