Variance Heterogeneity Research Articles

Spatiotemporal analysis of roadway terrains impact on large truck driver injury severity outcomes using random parameters with heterogeneity in means and variances approach

This study employs a partially temporally constrained modeling approach to examine spatiotemporal variations in driver injury severity in single-vehicle large truck crashes across different terrains in California, allowing for a nuanced understanding of how specific factors influencing injury outcomes may change over time. Utilizing crash data from January 1st, 2015, to December 31st, 2017, obtained from the Highway Safety Information System, this study categorizes terrains as flat, rolling, and mountainous terrain and employs a random parameter multinomial logit model with heterogeneity in means and variance to account for potential heterogeneity in crash injury outcomes. This approach helps understand how different terrains influence injury severities while allowing for parameter variability across observations. The analysis is further enriched by likelihood ratio tests to verify the stability and temporal transferability of the model estimates across different terrains and years. Notably, the study identifies truck overturning as the first and second event in a crash as a consistent parameter influencing injury severity across all years, emphasizing its importance regardless of terrain or time in single-vehicle large truck crashes. Furthermore, this study takes into account a wide range of variables, including driver characteristics, crash attributes, roadway characteristics, vehicle features, and environmental and temporal aspects. The findings highlight the importance of terrain-specific elements in traffic safety assessments and the need for focused measures to reduce serious injuries in truck crashes. The out-of-sample simulation revealed a significant increase in minor and severe injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains. This research not only contributes to the existing literature by detailing the dynamics of injury severity in single-vehicle large truck crashes but also announces the utility of partially temporally constrained models in enhancing traffic safety management strategies.

The integration of quantile regression with 3VmrMLM identifies more QTNs and QTNs-by-environment interactions using SNP and haplotype-based markers

Current methods used in genome-wide association studies frequently lack power due to their inability to detect heterogeneous associations and rare and multiallelic variants. To address these issues, quantile regression was integrated for the first time with a compressed variance component multi-locus random-SNP-effect mixed linear model (3VmrMLM) to propose q3VmrMLM for detecting heterogeneous quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs), while q3VmrMLM-Hap was designed to identify multiallelic haplotypes and rare variants. In Monte Carlo simulation studies, q3VmrMLM had higher power than 3VmrMLM, SKAT, and iQRAT. In the re-analysis of 10 traits in 1439 rice hybrids, 261 known genes were identified only by q3VmrMLM and q3VmrMLM-Hap, while 175 known genes were detected commonly by the new and existing methods. Of all the significant QTNs with known genes, q3VmrMLM (179: 140 variance heterogeneity and 157 quantile effect heterogeneity) found more heterogeneous QTNs than 3VmrMLM (123), SKAT (27) and iQRAT (29), q3VmrMLM-Hap (121) mapped more low-frequency (<0.05) QTNs than q3VmrMLM (51), 3VmrMLM (43), SKAT (11) and iQRAT (12), and q3VmrMLM-Hap (12), q3VmrMLM (16) and 3VmrMLM (12) had similar power in identifying gene-by-environment interactions. All significant and suggested QTNs achieved the highest predictive accuracy (r=0.9045). In conclusion, this study provides a new and complementary approach to mining genes and unrevealing the genetic architecture of complex traits in crops.

Association between gestational diabetes mellitus diagnostic criteria and adverse pregnancy outcomes—a systematic review and meta-analysis of adjusted effect sizes from studies using current diagnostic criteria

ObjectivesTo quantify the association between Gestational Diabetes Mellitus (GDM) and adverse pregnancy outcomes and primarily compare the associations between diagnostic criteria following the International Association of Diabetes and Pregnancy Study...

Optimizing the genetic evaluation criteria for the small herd of Saanen x Beetal crossbred dairy goats of Indian sub-tropic

This study focused on the accurate genetic evaluation of a small dairy flock of crossbred dairy goats, specifically developed for high milk production in the Indian sub-tropical climate. The study was conducted on Saanen x Beetal (SxB) crossbred goats, which utilized 12,660 test day records for first parity. Additionally, complete lactation records (N=1283) across multiple parities and for the first parity (N=659) were analyzed separately for genetic evaluation. The main traits examined were 150-day milk yield (150DMY), days in milk (DIM), peak yield (PY), and total milk yield (TMY), with averages of 198.80±2.83 kg, 227.90±4.18 days, 1.17±0.02 kg, and 262.40±6.12 kg, respectively, highlighting the genetic superiority of SB goats over native Indian goats. Across parities, the estimates for 150DMY, DIM, and TMY were 230±6.16 kg, 210±7.0 days, and 277±11.82 kg, respectively. Given the moderate heritability (0.24±0.08) and repeatability (0.29±0.04) estimates of 150DMY, the study recommends using 150DMY as the primary selection criterion for genetic improvement in SxB goats. The single-trait random regression model (RRM) utilizing various orders of orthogonal Legendre polynomials (LEG) and B-spline (BS) functions with heterogeneous residual variances was also employed. The test day milk yield (TDMY) showed a least squares mean of 1.30±0.01 kg, with moderate heritability estimates across test days (0.26±0.08). The optimal model was identified as a quadratic B-spline function with six knots (BS6Q). Positive genetic correlations were observed between consecutive test-day milk yield values, while correlations decreased for more distant test days. The study demonstrated the superiority of the B-spline model in genetically assessing Saanen x Beetal dairy goats, highlighting its benefits in curve fitting, genetic parameter estimation, and higher breeding value prediction accuracy. Looking into the moderate heritability and desirable genetic correlation of 150 DMY with other lactation traits, we recommend using 150 DMY for further selection programs. As the concordance of ranking between different approaches for breeding value prediction was high, we recommend using the random regression test day model (RR-TDM) when data recording across lactation is costly.

Why they take the risk to perform a direct left turn at intersections: A data-driven framework for cyclist violation modeling

Bicycle crashes at intersection areas are posed a worrying traffic safety issue, and one of the main reasons for bicycle crashes is failing to avoid conflicts with motor vehicles and other bicycles. Clearly, cyclists are more exposed to risk if they perform a direct left turn (DLT) being mixed with left-turning vehicle under a left-turn phase. Owing to the lack of exposure data, the detection of DLT event and the mechanism behind the risky riding behavior have yet to be discovered. To bridge these gaps, this study proposes a DLT detection framework based on bike sharing trajectories. Moreover, this study seeks to understand the contributing factors to DLT behavior using the random parameters logit model with heterogeneity in means and variances (RPLHMV) to account for unobserved heterogeneity in the DLT cases dataset. Statistical analysis shows that DLT is most likely to occur on weekdays during peak periods under large commuting demand. As to what caused the DLT violations, law-obeying cyclists are more susceptible to external events, while risk-taking cyclists are subtly undermined by their habits. In addition, the model of RPLHMV reveals several significant contributing factors to the propensity of DLT violations, such as event time, available passing time for left-turning bicycles, and average cycling speed, whereas the indicator variables of actual waiting time, available passing space for left-turning bicycles, and preference for DLT violation become the emerging influential variables. This study is expected to help better understand DLT occurrence and propose countermeasures more efficiently for reducing cyclists’ DLT rate.

An Exploratory Assessment of Driver Injury Severities in Large-Truck Crashes Involving Fatigued and Non-Fatigued Driving

Fatigue poses a persistent safety challenge for long-haul truck drivers, significantly elevating the risk of highway crashes and compromising daily work performance. This study investigates the factors influencing driver injury severity in single-large truck crashes due to fatigued driving, compared with non-fatigued driving (normal driving). Using Florida crash data from 2011 to 2019 (inclusive), covering both fatigue and non-fatigue-related crashes, this analysis employs random parameters logit models to account for potential unobserved heterogeneity in means and variances. A comprehensive array of factors affecting driver injury severity, including spatial and temporal characteristics, vehicle and traffic attributes, roadway conditions, and driver-specific characteristics, are examined. Strikingly, different parameter estimates emerge for fatigue and non-fatigue-related crashes, indicating fundamental dissimilarities in unobserved heterogeneity in large truck crash data. The estimated model results unveil distinct marginal effects between fatigued and non-fatigued driving, particularly concerning severe injury crashes, suggesting significant variation in driver behavior based on fatigue levels. These findings contribute substantially to the growing literature on the intrinsic disparities between fatigued and non-fatigued driving behaviors. Moreover, this research underscores the potential ramifications of these distinctions on the safety performance of commercial trucks, highway safety technologies, and policy-related safety countermeasures. Understanding the unique characteristics of fatigued driving can inform targeted interventions to bolster safety within the commercial trucking industry and mitigate the impact of severe crashes resulting from fatigued driving.

Machine Learning for Prediction of Resistance Scores in Wheat (Triticum aestivum L.)

ABSTRACTMachine learning methods were shown to improve the prediction accuracies of genomic prediction of resistance scores compared to methods like RR‐BLUP, which were originally designed for metric rather than ordinal response values. We conducted a cross‐validation study with 361 wheat genotypes evaluated for five fungal diseases. Our objective was to compare the prediction accuracy and the ability to identify the most resistant genotypes of 19 genomic prediction approaches. Each approach consisted of a different combination of prediction method (RR‐BLUP, an alternative method with heterogeneous marker variances, Bayesian generalized linear regression with an ordinal response, support vector machine, gradient boosting machine and random forest), predictor (single SNP markers, LD‐based haplotype blocks, 250 variables generated with an autoencoder and SNPs identified with incremental feature selection) and response value (untransformed and logit‐transformed resistance scores). In our dataset, RR‐BLUP was consistently among the methods with the largest prediction accuracies and the best abilities to identify resistant genotypes in four of five investigated traits. However, in P. triticina, using gradient boosting machine and random forest instead of RR‐BLUP increased the prediction accuracy from 0.64 to 0.71, indicating that machine learning methods may have an advantage over linear models in genomic prediction. We also found that even though there was a positive correlation between the prediction accuracy and Cohen's , a measure to judge how well the most resistant genotypes can be identified, the correlation is not perfect and a large value for the prediction accuracy does not necessarily translate into an equally large value.

Causal role of immune cells in muscle atrophy: mendelian randomization study

Immune system and inflammation had a great influence on the progression of muscle atrophy. However, the causal relationship with specific immune cell traits remained uncertain. The aim of this study was to elucidate the genetic influences on these associations, providing insights into the underlying mechanisms of muscle atrophy. A bidirectional two-sample Mendelian randomization (MR) analysis was conducted to investigate the causal relationship between immune cell phenotypes and muscle atrophy. Data on immune cell phenotypes were obtained from a research cohort containing data on 731 immune cell phenotypes and data on muscle atrophy were sourced from a Finnish database. MR analysis was performed using the MR-Egger method, weighted median, inverse variance weighting, heterogeneity testing, sensitivity analysis, and multiplicity analysis, with results subjected to false discovery rate(FDR) correction. Additionally, the UK Biobank cohort was utilized as an external validation. A total of 31 immune phenotypes with causal relationships with muscle atrophy were identified, including various phenotypes of conventional dendritic cells, myeloid cells, T cells/B cells/natural killer cells, regulatory cells, and T cell maturation stages. Among them, 12 immune phenotypes were identified as exhibiting a positive causal relationship with muscle atrophy, while 19 immune phenotypes were demonstrated to have a negative causal association, highlighting the complex interactions between immune cells and muscle health. The results of the reverse MR analysis indicated that a negative correlation between muscle atrophy and CD28 on secreting Treg (OR = 0.9038, 95%CI:0.8308 ~ 0.9832, P = 0.0186). A significant positive correlation was revealed by external datasets between the CD25 on IgD + CD38- immune phenotype and the risk of muscle atrophy, which was consistent with the trend observed in the training group (OR = 1.1041, 95% CI: 1.1005–1.1076, P = 0.0263). No evidence of pleiotropy was observed, and the reliability of these findings was demonstrated by the leave-one-out analysis. The findings highlight significant correlations between certain immune cell features and muscle atrophy, providing potential targets for further investigation of immunological mechanisms and therapeutic interventions for this condition.

Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts.

Determinants of driver injury severity in electric vehicle crashes: A random parameters binary probit model with heterogeneity in means and variances

This study aims to investigate the factors significantly affecting occupant injury severity in electric vehicle (EV) accidents, with a particular focus on traffic flow characteristics before accidents, which have often been overlooked in previous research. Using data on EV accidents from January 1, 2021, to December 31, 2021, in Guangdong Province, China, this study considers various vehicle, road, weather, and pre-accident traffic flow characteristics. The research employs the random parameters binary probit model with heterogeneity in means and variances, comparing it with the binary probit model and the random parameters binary probit model to comprehensively address unobserved heterogeneity and further explain the factors contributing to the randomness of random variables. The results indicate that the random parameters binary probit model with heterogeneity in means and variances provides a superior fit. The findings reveal that variables such as the time of day, type of vehicle involved in the collision, color of EV, average morning/evening peak traveling hours significantly impact the severity of injuries in EV accidents. Additionally, the black indicator exhibits heterogeneity in mean and variance during the afternoon period. This study offers an in-depth understanding of the mechanisms behind driver injury severity in EV accidents and contributes to the development of effective countermeasures to protect drivers from severe injuries.

Underpowered studies in muscle metabolism research: Determinants and considerations

Biomedical research frequently employs null hypothesis testing to determine whether an observed difference in a sample is likely to exist in the broader population. Null hypothesis testing generally assumes that differences between groups or interventions are non-existent, unless proven otherwise. Because biomedical studies with human subjects are often limited by financial and logistical resources, they tend to have low statistical power, i.e. a low probability of statistically confirming a true difference. As a result, small but potentially clinically important differences may be overseen or ignored simply due to the absence of a statistically significant difference. This absence is often misinterpreted as ‘equivalence’ of treatments. In this educational paper, we will use practical examples related to the effects of exercise and nutrition on muscle protein metabolism to illustrate the most important determinants of statistical power, as well as their implications for both investigators and readers of scientific articles.Changes in muscle mass occur at a relatively slow rate, making it practically challenging to detect differences between treatment groups in a long-term setting. One way to make it ‘easier’ to differentiate between groups and hence increase statistical power is to have a sufficiently long study duration to allow treatment effects to become apparent. This is especially relevant when comparing treatments with relatively small expected differences such as the effect of modest changes in daily protein intake. Secondly, one could try to minimize the variance and response heterogeneity within groups, for example by using strict inclusion criteria and standardization protocols (e.g., meal provision), by using cross-over designs, or even within-subject designs where two interventions are compared simultaneously (e.g., studying an exercised limb vs a contralateral control limb) although this might limit the generalizability of the findings (e.g. such single-limb exercise training is not common in practice). In terms of data interpretation, investigators should obviously refrain from drawing strong conclusions from underpowered studies. Yet, such studies still provide valuable data for meta-analyses. Finally, because muscle protein synthesis rates are highly responsive to anabolic stimuli, acute metabolic studies are more sensitive to detect potentially clinically relevant differences in the anabolic response between treatments. Apart from further elaborating on these topics, this educational article encourages readers to more critically question null findings and scientists to more clearly discuss limitations that may have compromised statistical power.

Seasonal variations and temporal instability of motorcyclist injury severity in Cambodia: Analyses based on a random parameter logit model with heterogeneity in means and variances

Motorcycles are a prevalent mode of transportation in countries like Cambodia that experience distinct rainy and dry seasons. However, the safety concerns associated with motorcycling in this region have not been thoroughly investigated. This study addresses this research gap by examining the severity of motorcyclist injuries in Cambodia, considering the potential variations across seasons and the fluctuations in contributing factors over time. Utilizing a random parameter logit model with heterogeneity in means and variances, the research analyzes motorcycle crash data from 2015 to 2017 to identify heterogeneities in the determinants of injury severity. The study confirms seasonal variations and temporal instabilities in influential factors, highlighting the need for distinct modeling for dry and rainy seasons due to varying contributing factors. Key findings include the consistent increase in fatal injury risk associated with head-on collisions and elderly rider involvement across both seasons. During the rainy season, motorcycle-to-motorcycle crashes significantly heighten the likelihood of severe injuries, with weekend crashes more likely to result in fatalities. Furthermore, more than half of speeding incidents during the rainy season consistently led to fatal injuries across all three years. In contrast, during the dry season, riders faced a greater risk of severe injuries compared to pillion riders, with crashes on national roads more likely to lead to fatal outcomes. Temporal stability tests further reveal that the influence of external variables on motorcyclist injury severity varies across years, stressing the need for tailored, season-specific approaches to effectively mitigate and prevent crashes.

Effect of telemedicine interventions on weight loss: A systematic review and meta-analysis.

Obesity and its associated health challenges represent a pressing global concern. Telemedicine interventions offer a promising solution for effective weight loss support. This study examines the impact of telemedicine interventions on weight loss support. We conducted a search of PubMed, Scopus, and Web of Science, starting from their inception. Both researchers systematically selected articles and extracted data using a designated data collection form. To assess the risk of bias in the included studies, we employed the Mixed Methods Appraisal Tool. Publication bias was evaluated through funnel plots and Egger's and Begg's tests. Utilizing the gathered data, we computed the standardized mean differences (Hedges's g) between the treatment and control groups. We estimated heterogeneity variance using the Q test and I2 statistic. The analysis was carried out using Stata 17.0. Out of a total of 2626 retrieved articles, 30 studies were included in the analysis. Telemedicine interventions can cause weight loss in people (Hedges's g = 0.09, 95% CI: -0.13, 0.39; p-value < 0.001). The type of telemedicine intervention and target population were a significant moderator of the heterogeneity between studies (p < 0.05). This study highlights the potential of telemedicine interventions as valuable tools in weight loss programs. Embracing these technologies can enhance the effectiveness of weight management strategies for diverse populations.

Heterogeneity-aware integrative regression for ancestry-specific association studies.

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.

Understanding the role of the COVID-19 pandemic on risky driving behavior and injury severity of drivers: Embracing heterogeneity in means and variances

The onset of the COVID-19 pandemic significantly altered global mobility patterns, leading to a marked decrease in travel activities worldwide. In the United States, travel demand fell notably, contributing to a 22% reduction in overall crashes in 2020 compared to the prior year. In Oregon, vehicle miles traveled dropped by 10.8%, and crashes decreased by 23.9%, yet fatalities increased by 2.63%. This rise in fatal crashes is linked to altered driving behaviors, including aggressive, distracted, and impaired driving.This study investigates factors related to risky driving behavior-induced crashes in Oregon during the pandemic. Utilizing a random parameters multinomial logit model that accommodates heterogeneity, we found significant correlations between reckless behaviors—such as driving without a license, speeding, and neglecting to use restraints—and the severity of injuries. Our findings indicate temporal instability in factors contributing to injury severity. In 2019, severe injuries were more common in crashes involving drug use, drivers aged 45–54, and in speed zones of 45–55 mph. In 2020, young drivers under 25 and night-time crashes on lit streets were more likely to result in severe injuries. This research sheds light on the impact of COVID-19 on driver behavior and injury severity, particularly concerning aggressive driving. The identified risk factors are crucial for state and federal agencies to enhance road safety measures and ensure safer environments for all road users.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Meat tenderness in Australian lamb: Data editing, environmental variation and their effects in genetic parameter estimation

Breeding for meat quality increases the value of lambs and requires reliable genetic parameters to achieve balanced genetic progress. Meat tenderness, accomplished by selecting for lower shear force, is an important eating quality trait because of its relationship with consumer satisfaction. Factors influencing shear force, include the pH and temperature decline post-mortem which can contribute towards higher shear force values and increased variation across contemporary groups. This study explored if genetic parameters for shear force change when post slaughter covariates and heterogeneous variance are corrected for, using data from 32,223 animals from different sheep breeds. Results showed that removing extreme individuals and contemporary groups with high mean shear force values reduced residual variance, followed by a smaller reduction in additive genetic variance and little effect on heritability. Results show that edited data performed better at predicting progeny performance and reduced potential bias introduced in the genetic evaluation due to data quality. The effect of including post-slaughter covariates in the genetic analysis was tested by estimating different model predictability through regression of estimated breeding values against offspring performance, showing that the model including hot carcass weight performed better followed by the one including both carcass weight and C-site fat depth. Our results highlight that historic and current in-plant recording practices do not provide the capacity to account for non-genetic factors associated with abattoir environment that might be impacting the ability to accurately calculate shear force breeding values. In that sense, genetic evaluation can be improved by applying more rigorous data editing.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety.

118 DHGLMF90: A software tool using double hierarchical generalized linear models for estimating the genetic heterogeneity of residual variance

Abstract In animal breeding, there is a growing interest in selecting animals that exhibit uniform responses to environmental conditions, aiming for product consistency as a desirable breeding objective. This could be achieved by modeling heteroskedastic residuals when estimating breeding values through a hierarchical single-trait model that includes a mean and dispersion part. The latter could be influenced by genetic and non-genetic features. Bayesian methods can estimate heteroskedastic residuals using the Metropolis-Hastings algorithm, which is a slow and inefficient approach. Double hierarchical generalized linear models (DHGLM) provide a faster alternative to Bayesian methods. This study aimed to develop software named DHGLMF90 to implement DHGLM using a highly efficient algorithm to accurately estimate heterogeneous residual variances and breeding values, particularly suited for handling large datasets. We improved the reweighted least squares algorithm (IRWLS) to enhance convergence properties compared with prior implementations. IRWLS iteratively computes variance components and leverages them for a bivariate model, covering both the mean and dispersion part of the original model. In DHGLMF90, variance component estimation is performed using REML through BLUPF90+. The software was tested using both simulated and real datasets, the latter from previous studies. Additionally, the simulations were replicated to ensure a comprehensive evaluation of the DHGLMF90 performance. Results indicated that, in most cases, there were no significant differences in estimation compared with results obtained from previous studies. DHGLMF90 converged in order of magnitude faster than Bayesian methods. Future developments will include analyzing datasets with genomic information and multiple-trait models.

Disparate trajectories of cognitive aging among American Indian and Alaskan Native people with and without HIV.

This study describes trajectories of cognitive aging among American Indian/Alaskan Native (AI/AN) adults with and without HIV and the role of immunosenescence longitudinally. We characterized trajectories of cognitive aging in a sample of 333 AI/AN and 309 non-Hispanic White (NHW) adults who were followed longitudinally for up to 20 years by the HIV Neurobehavioral Research Program (HNRP) across six U.S. research sites. We used growth curve modeling with autoregressive Lag-1 structures and heterogeneous residual variances to assess the role of ethnoracial identity and HIV grouping upon decline in trajectories of cognitive aging. HIV- AI/AN adults demonstrated earlier and steeper decline in normative trajectories of cognitive aging on tasks of processing speed, timed tasks of attention/working memory, executive function, and psychomotor speed in comparison to HIV- NHW adults. Accentuated trajectories of cognitive aging were evident in both HIV+ and HIV+ immunosuppressed groups in comparison to HIV- peers and were primarily driven by the role of immunosenescence. AI/AN disparities in trajectories of cognitive aging are evident and are likely explained by the interplay of biopsychosociocultural factors, including immunosenescence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).