Residual Analysis for Linear Mixed Models

Associations between cannabis use and same-day health and substance use behaviors

OBJECTIVE

Cannabis use has increased in the United States as states have legalized/decriminalized recreational and/or medicinal use. The primary aim of this study was to examine daily associations between cannabis use and health (physical activity [PA]) and substance use (alcohol consumption, cigarette use) behaviors.

METHOD

Data from a 28-day nationwide study that prompted daily ecological momentary assessments (EMAs) were used to assess daily cannabis use, moderate-to-vigorous PA (MVPA), alcohol containing drinks consumed, and cigarettes smoked. Only participants who reported cannabis use on at least one day during the study period were included in the analysis (N = 98). Generalized linear mixed models were used to examine associations between cannabis use and same-day health and substance use behaviors while adjusting for race, biological sex, and age.

RESULTS

Daily cannabis use was positively associated with daily PA (p = 0.04, 3.31-minute higher PA duration on use vs. non-use days), number of alcohol containing drinks consumed (p = 0.01, 0.45 more drinks on use vs. non-use days), and number of cigarettes smoked (p = 0.01, 0.63 more cigarettes on use vs. non-use days).

CONCLUSIONS

This study was among the first to use EMAs to examine associations between daily cannabis use and same-day PA, alcohol consumption, and cigarette use. Overall, findings indicated that daily cannabis use is associated with higher engagement in these daily health and substance use behaviors. Further research is needed to understand mechanisms linking cannabis use with these and other behaviors.

Maternal concentrations of perfluoroalkyl sulfonates and alterations in white matter microstructure in the developing brains of young children

BACKGROUND

Maternal exposure to per- and polyfluoroalkyl substances (PFAS) has been linked to child neurodevelopmental difficulties. Neuroimaging research has linked these neurodevelopmental difficulties to white matter microstructure alterations, but the effects of PFAS on children's white matter microstructure remains unclear. We investigated associations between maternal blood concentrations of six common perfluoroalkyl sulfonates and white matter alterations in young children using longitudinal neuroimaging data.

METHODS

This study included 84 maternal-child pairs from a Canadian pregnancy cohort. Maternal second trimester blood concentrations of perfluorohexanesulfonate (PFHxS) and five perfluorooctane sulfonate (PFOS) isomers were quantified. Children underwent magnetic resonance imaging scans between ages two and six (279 scans total). Adjusted linear mixed models investigated associations between each exposure and white matter fractional anisotropy (FA) and mean diffusivity (MD).

RESULTS

Higher maternal concentrations of perfluoroalkyl sulfonates were associated with higher MD and lower FA in the body and splenium of the corpus callosum of young children. Multiple sex-specific associations were found. In males, PFHxS was negatively associated with FA in the superior longitudinal fasciculus, while PFOS isomers were positively associated with MD in the inferior longitudinal fasciculus (ILF). In females, PFOS isomers were positively associated with FA in the pyramidal fibers and MD in the fornix, but negatively associated with MD in the ILF.

CONCLUSION

Maternal exposure to perfluoroalkyl sulfonates may alter sex-specific white matter development in young children, potentially contributing to neurodevelopmental difficulties. Larger studies are needed to replicate these findings and examine the neurotoxicity of these chemicals.

Critical reproductive behaviors in Scaled Quail and Northern Bobwhite are affected by thermal variability and mean temperature

Animals can respond differently to shifting thermal variability versus thermal averages, both of which are changing due to climate warming. How these thermal variables affect parental care behaviors can reveal the ability of parents to modify their behaviors to meet the competing demands of their offspring's thermal needs and self-maintenance, which becomes critical in suboptimal thermal conditions. Further, the time frame used to examine the interplay between temperature and behavioral shifts (e.g., seasonal patterns in care vs. drivers of individual care decisions) can provide different information about the plasticity of parental care behavior. We investigated the relationship between thermal means, thermal variability, and incubation behaviors across multiple timescales in Scaled Quail and Northern Bobwhite. Both species decreased off-bout length during periods of high thermal variability, a novel finding among studies of avian parental behavior. Further relationships between thermal endpoints (mean vs. variation) and behavior differed depending on the temporal scale. For instance, total daily time spent off the nest was not influenced by daily average temperature, yet individual off-bout duration increased with increasing average temperature in the 2 h prior to the off-bout. These results provide evidence that thermal-behavioral relationships differ across scales and likely represent a bird's ability to modify their incubation strategy to rapidly respond to the immediate thermal environment (altering individual off-bout length based on temperature) to meet self-maintenance needs while resulting in a similar outcome for their nest (total daily off-bout time). However, longer off-bout durations during high temperature events can come with reproductive costs, sometimes resulting in acute offspring mortality when eggs or chicks experience lethal temperatures.

Diagnostics for categorical response models based on quantile residuals and distance measures

Polytomous categorical data are frequent in studies, that can be obtained with an individual or grouped structure. In both structures, the generalized logit model is commonly used to relate the covariates on the response variable. After fitting a model, one of the challenges is the definition of an appropriate residual and choosing diagnostic techniques. Since the polytomous variable is multivariate, raw, Pearson, or deviance residuals are vectors and their asymptotic distribution is generally unknown, which leads to difficulties in graphical visualization and interpretation. Therefore, the definition of appropriate residuals and the choice of the correct analysis in diagnostic tools is important, especially for nominal data, where a restriction of methods is observed. This paper proposes the use of randomized quantile residuals associated with individual and grouped nominal data, as well as Euclidean and Mahalanobis distance measures, as an alternative to reduce the dimension of the residuals. We developed simulation studies with both data structures associated. The half-normal plots with simulation envelopes were used to assess model performance. These studies demonstrated a good performance of the quantile residuals, and the distance measurements allowed a better interpretation of the graphical techniques. We illustrate the proposed procedures with two applications to real data.

Effects of Miosis on the Visual Acuity Space under Varying Conditions of Contrast and Ambient Luminance in Presbyopia

Background: Presbyopia is an age-related ocular condition, typically affecting individuals aged over 40 years, characterized by a gradual and irreversible decline in the eye's ability to focus on nearby objects. Correction methods for presbyopia encompass the use of corrective lenses, surgical interventions (corneal or lens based), and, more recently, the FDA-approved topical administration of 1.25% pilocarpine. While prior research has demonstrated the efficacy of daily pilocarpine eye drop application in enhancing near visual acuity by increasing the depth of focus leveraging the pinhole effect, limited knowledge exists regarding its influence on visual acuity under varying conditions of contrast and ambient luminance. Methods: This study aims to investigate the impact of these variables on visual acuity, employing the VA-CAL test, among 11 emmetropic and 11 presbyopic volunteers who reported subjective difficulties with near vision. This study includes evaluations under natural conditions with a pinhole occluder (diameter of 2 mm), and subsequent administration of 1% pilocarpine (Pilomann, Bausch + Lomb, Laval, Canada). Results: The VA-CAL results demonstrate the expected, statistically significant effects of contrast and ambient luminance on visual acuity in both emmetropic and presbyopic volunteers. Furthermore, in emmetropic individuals, the application of pilocarpine resulted in a statistically significant reduction in visual acuity. In contrast, presbyopes did not exhibit statistically significant differences in the visual acuity space under either the pinhole or pilocarpine conditions when compared to natural conditions. Conclusions: The pharmacological treatment of presbyopia with pilocarpine eye drops, intended to enhance near vision, does not adversely affect visual acuity in presbyopes. This suggests that pilocarpine may offer a viable alternative for individuals averse to wearing corrective eyewear.

A hierarchical approach for evaluating athlete performance with an application in elite basketball

In this paper, we present the ON score for evaluating the performance of athletes and teams that includes a season-long evaluation system, a single-game evaluation, and an evaluation of an athlete's overall contribution to their team. The approach used to calculate the ON score is based on mixed-effects regression models that take into account the hierarchical structure of the data and a principal component analysis to calculate athlete rating. We apply our methodology to a large dataset of National Basketball Association (NBA) games spanning four seasons from 2015-2016 to 2018-2019. Our model is validated using two systematic approaches, and our results demonstrate the reliability of our approach to calculate an athlete's performance. This provides coaches, General Managers and player agents with a powerful tool to gain deeper insights into their players' performance, make more informed decisions and ultimately improve team performance. Our methodology has several key advantages. First, by incorporating the hierarchical structure of the data, we can obtain valuable information about an athlete's contribution within their team. Second, the use of principal component analysis allows us to calculate a single score, the ON score, that captures the overall performance of an athlete. Third, our approach is based on classical restricted likelihood methods, which makes the calculation faster than Bayesian methods typically requiring 1000 posterior samples. With our approach, coaches and managers can evaluate athletes' performance throughout the season, compare athletes and teams over a year, and assess an athlete's performance during a single game. Our methodology can also complement other ratings and box score metrics to provide a more comprehensive assessment of an athlete's performance as our method uses the hierarchical nature of performance data (i.e. player nested within team over season) which is typically ignored in player rating systems. In summary, our methodology represents a significant contribution to the field of sports analytics and provides the foundation for future developments.

Statistical modeling of diffusive CO2 emissions before the creation of the SINOP hydroelectric reservoir, Brazil

Abstract Several discussions have arisen about energy from hydroelectric plants being considered clean energy and its reservoirs have been investigated due to the large emission of greenhouse gases (GHG), such as carbon dioxide, methane, and nitrous oxide. The present work shows a statistical study of the diffusive CO2 emissions before the formation of the reservoir of the hydroelectric power plant (HPP) of SINOP, Brazil. The association between emissions collected at the surface (water-air) and at the bottom of the reservoir (sediment-water) was investigated during four data collection campaigns, carried out from November 2017 to September 2018. This study aims to compare the effect of reservoir depth on the diffusive flow of CO2 at 34 collection points. The variable depth analyzed was defined from points collected on the surface and bottom of the reservoir. The objective is to detect whether different periods of time and whether the depth of the reservoir have a direct impact on the behavior of diffusive CO2 emissions. As the measurements of the observational unit are repeatedly observed, there is a multilevel structure, individuals are independent of each other, but there is an intra-individual correlation. Considering this data configuration, an estimation of generalized equations (GEE) was performed, which is a technique that estimates the intra-individual correlation matrix and thus produces estimates for the parameters of the generalized regression models (Generalized Regression Models – GLM) that are not biased. The study showed that the average diffusive CO2 emissions are higher on the reservoir surface. The study also found that, on average, there are more emissions during the rainy season in the region than during the dry season.

Using residual regressions to quantify and map signal leakage in genomic prediction

BACKGROUND

Most genomic prediction applications in animal breeding use genotypes with tens of thousands of single nucleotide polymorphisms (SNPs). However, modern sequencing technologies and imputation algorithms can generate ultra-high-density genotypes (including millions of SNPs) at an affordable cost. Empirical studies have not produced clear evidence that using ultra-high-density genotypes can significantly improve prediction accuracy. However, (whole-genome) prediction accuracy is not very informative about the ability of a model to capture the genetic signals from specific genomic regions. To address this problem, we propose a simple methodology that detects chromosome regions for which a specific model (e.g., single-step genomic best linear unbiased prediction (ssGBLUP)) may fail to fully capture the genetic signal present in such segments-a phenomenon that we refer to as signal leakage. We propose to detect regions with evidence of signal leakage by testing the association of residuals from a pedigree or a genomic model with SNP genotypes. We discuss how this approach can be used to map regions with signals that are poorly captured by a model and to identify strategies to fix those problems (e.g., using a different prior or increasing marker density). Finally, we explored the proposed approach to scan for signal leakage of different models (pedigree-based, ssGBLUP, and various Bayesian models) applied to growth-related phenotypes (average daily gain and backfat thickness) in pigs.

RESULTS

We report widespread evidence of signal leakage for pedigree-based models. Including a percentage of animals with SNP data in ssGBLUP reduced the extent of signal leakage. However, local peaks of missed signals remained in some regions, even when all animals were genotyped. Using variable selection priors solves leakage points that are caused by excessive shrinkage of marker effects. Nevertheless, these models still miss signals in some regions due to low linkage disequilibrium between the SNPs on the array used and causal variants. Thus, we discuss how such problems could be addressed by adding sequence SNPs from those regions to the prediction model.

CONCLUSIONS

Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.

Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial

PURPOSE

A joint modeling approach is recommended for analysis of longitudinal health-related quality of life (HRQoL) data in the presence of potentially informative dropouts. However, the linear mixed model modeling the longitudinal HRQoL outcome in a joint model often assumes a linear trajectory over time, an oversimplification that can lead to incorrect results. Our aim was to demonstrate that a more flexible model gives more reliable and complete results without complicating their interpretation.

METHODS

Five dimensions of HRQoL in patients with esophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 were analyzed. Joint models assuming linear or spline-based HRQoL trajectories were applied and compared in terms of interpretation of results, graphical representation, and goodness of fit.

RESULTS

Spline-based models allowed arm-by-time interaction effects to be highlighted and led to a more precise and consistent representation of the HRQoL over time; this was supported by the martingale residuals and the Akaike information criterion.

CONCLUSION

Linear relationships between continuous outcomes (such as HRQoL scores) and time are usually the default choice. However, the functional form turns out to be important by affecting both the validity of the model and the statistical significance.

TRIAL REGISTRATION

This study is registered with ClinicalTrials.gov, number NCT00861094.

A linear mixed model to estimate COVID-19-induced excess mortality

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Perceptions of livestock value chain actors (VCAs) on the risk of acquiring zoonotic diseases from their livestock in the central dry zone of Myanmar

OBJECTIVES

The Central Dry Zone (CDZ) is one of the most important livestock production areas of Myanmar. However, there is an eminent lack of information on the attitudes and traditional beliefs of local farmers and livestock supply chain actors in CDZ of Myanmar on the public health implications. A modified data collection instrument of the Health Belief model was developed to investigate attitudes, beliefs and barriers to the application of recommended zoonotic disease prevention.

STUDY DESIGN

Cross-sectional study.

METHOD

Data analyses were conducted considering a two-phase multilevel mixed effect binomial generalized linear models modelling approach.

RESULTS

The availability of information about zoonosis to supply chain actors influenced their confidence to implement preventive actions (OR = 1.5, p = 0.045 for cattle diseases; OR = 1.5, p = 0.022 for village chicken diseases). Supply chain actors were more likely aware of zoonosis transmitted by cattle compared to livestock farmers (OR = 0.3, p = 0.005 for cattle farmers), while people not rearing or trading small ruminants and/or poultry were less likely to be aware of the zoonotic risk associated with these animals (p < 0.005). Information on zoonosis transmitted from small ruminants was mainly promoted through farmers (p = 0.032), while information on zoonotic diseases that can be obtained from chickens was disseminated through farmers, local authorities and the media. Nevertheless, appropriate hand hygiene measures (i.e. cleaning of hands after touching, cutting, cooking meat) (OR = 7.7, p < 0.001 for zoonotic small ruminant diseases; OR = 1.6, p = 0.073 for zoonotic village chicken diseases) and treating of sick animals (OR = 7.3, p < 0.001 for small ruminant zoonotic diseases; OR = 2.2, p = 0.031 for village chicken zoonotic diseases) increased the confidence of small ruminant and village chicken owners to prevent these zoonotic infections.

CONCLUSIONS

The findings from this study indicate that while gender and the availability of information on zoonotic risks play an important role on the perceived threat of zoonoses, the practice of prevention methods influenced the confidence of value chain actors (VCAs) on zoonoses prevention.

Influence of Job Insecurity on Musculoskeletal Disorders: A Mediation Model with Nursing Aides

Work-related musculoskeletal disorders are some of the most prevalent diseases in the world. They have a multifactorial aetiology encompassing ergonomic and psychosocial risk factors. The aim of this study is to analyse the way job insecurity and physical workload are linked to musculoskeletal disorders, as well as the mediating role other psychosocial work risks can have on this potential relationship. A parallel mediation path regression analysis was designed using a sample of 457 nursing aides. The influence job insecurity and physical workload has on the onset of musculoskeletal symptoms together with the variables which mediate this relationship were examined. The results prove that both independent variables explain the onset of musculoskeletal symptoms in these professionals. The influence exerted by job insecurity is mediated by the social support received at work and the demands of the job. However, when analysing the physical workload, the social support received at work is not relevant as a mediator in this relationship. Job insecurity and physical workload are significant variables on the occurrence of musculoskeletal symptoms. The data obtained supports the need to focus on physical and psychosocial factors in order to prevent these disorders.

Effects of a Long-Term Adapted Judo Program on the Health-Related Physical Fitness of Children with ASD

Physical fitness is one of the most important physical and mental health aspects for children with Autism Spectrum Disorder (ASD). This study aimed to test the effects of a long-term adapted judo program on the health-related physical fitness of children with ASD. The participants were recruited from various associations of families and schools for children with special needs. Twenty-one children were assigned to an experimental group and nineteen to a control group. The experimental group participated in a six-month adapted judo program consisting of 90 min of practice each week. Health-related physical fitness was measured using the indicators obtained from the ALPHA-fitness battery, the estimated VO₂max and the waist/height ratio^0.5. Changes within and between groups were analyzed using linear mixed models for repeated measures designs and test-retest reliability of tests requiring a maximum score using the Intraclass Correlation Coefficient (ICC). A judo program tailored for children with ASD can improve the cardio-metabolic health and cardiorespiratory fitness of its participants. The problems involved with administering physical aptitude tests that involve maximum effort or performance in children with ASD cast serious doubts on the reproducibility of their results.

Performance of the Deep Neural Network Ciloctunet, Integrated with Open-Source Software for Ciliary Muscle Segmentation in Anterior Segment OCT Images, Is on Par with Experienced Examiners

Anterior segment optical coherence tomography (AS-OCT), being non-invasive and well-tolerated, is the method of choice for an in vivo investigation of ciliary muscle morphology and function. The analysis requires the segmentation of the ciliary muscle, which is, when performed manually, both time-consuming and prone to examiner bias. Here, we present a convolutional neural network trained for the automatic segmentation of the ciliary muscle in AS-OCT images. Ciloctunet is based on the Freiburg U-net and was trained and validated using 1244 manually segmented OCT images from two previous studies. An accuracy of 97.5% for the validation dataset was achieved. Ciloctunet's performance was evaluated by replicating the findings of a third study with 180 images as the test data. The replication demonstrated that Ciloctunet performed on par with two experienced examiners. The intersection-over-union index (0.84) of the ciliary muscle thickness profiles between Ciloctunet and an experienced examiner was the same as between the two examiners. The mean absolute error between the ciliary muscle thickness profiles of Ciloctunet and the two examiners (35.16 µm and 45.86 µm) was comparable to the one between the examiners (34.99 µm). A statistically significant effect of the segmentation type on the derived biometric parameters was found for the ciliary muscle area but not for the selective thickness reading ("perpendicular axis"). Both the inter-rater and the intra-rater reliability of Ciloctunet were good to excellent. Ciloctunet avoids time-consuming manual segmentation, thus enabling the analysis of large numbers of images of ample study cohorts while avoiding possible examiner biases. Ciloctunet is available as open-source.

Robust identification of temporal biomarkers in longitudinal omics studies

MOTIVATION

Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject.

RESULTS

In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups.

AVAILABILITY AND IMPLEMENTATION

We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Seed choice in ground beetles is driven by surface-derived hydrocarbons

Ground beetles (Coleoptera: Carabidae) are among the most prevalent biological agents in temperate agroecosystems. Numerous species function as omnivorous predators, feeding on both pests and weed seeds, yet the sensory ecology of seed perception in omnivorous carabids remains poorly understood. Here, we explore the sensory mechanisms of seed detection and discrimination in four species of omnivorous carabids: Poecilus corvus, Pterostichus melanarius, Harpalus amputatus, and Amara littoralis. Sensory manipulations and multiple-choice seed feeding bioassays showed olfactory perception of seed volatiles as the primary mechanism used by omnivorous carabids to detect and distinguish among seeds of Brassica napus, Sinapis arvensis, and Thlaspi arvense (Brassicaceae). Seed preferences differed among carabid species tested, but the choice of desirable seed species was generally guided by the olfactory perception of long chain hydrocarbons derived from the seed coat surface. These olfactory seed cues were essential for seed detection and discrimination processes to unfold. Disabling the olfactory appendages (antennae and palps) of carabid beetles by ablation left them unable to make accurate seed choices compared to intact beetles.

Ground beetles are generalist predators of various arable weed seeds. Sensory manipulations and multiple-choice feeding bioassays show that seed choice is stimulated by volatile chemicals derived from the epicuticular lipids on the seed coat.

Comparison of CRT and LCD monitors for objective estimation of visual acuity using the sweep VEP

Purpose

To investigate the applicability of liquid crystal displays (LCD) as suitable replacement for cathode ray tube monitors (CRT) as stimulator for the sweep VEP for estimating visual acuity.

Methods

In a first experiment, sweep VEPs were recorded in 13 healthy volunteers with best-corrected visual acuity with an LCD and a CRT monitor, respectively. Time-to-peak after stimulus and peak-to-trough amplitudes as well as the visual acuity, estimated using a second-order polynomial and the modified Ricker model, were compared between both monitor types. In a second experiment, sweep VEPs were recorded in six healthy volunteers with two levels of stimulus contrast using artificially reduced visual acuities as well as best-corrected with the same monitors as in the first experiment and additionally, a modern LCD gaming monitor with a response time of 1 ms. Time-to-peak after stimulus and peak-to-trough amplitudes were compared between the different combinations of monitors and contrasts. Finally, visual acuities estimated using the modified Ricker model were compared to subjective visual acuities determined using the Freiburg Visual Acuity and Contrast Test (FrACT).

Results

In the first experiment, the time-to-peak after stimulus presentation was statistically significantly delayed for LCD displays (mean difference [confidence interval]: 60.0 [54.0, 65.9] ms; t(516) = 19.7096, p < 0.0001). Likewise, peak-to-trough amplitudes were statistically significantly smaller for the LCD stimulator, however, not clinically relevant (mean difference [confidence interval]: − 0.89 [– 1.59, − 0.20] µV; t(516) =  − 2.5351, p = 0.0115). No statistically significant effect of the monitor type on the estimated visual acuity was found for neither method, second-order polynomial, nor the modified Ricker model. In the second experiment, statistically significant delays of the time-to-peak after stimulus onset were found for all combinations of monitor and contrast compared to the CRT monitor. A statistically significant, but not clinically relevant, difference of the peak-to-trough amplitudes was only found between the CRT monitor and the LCD gaming monitor (mean difference [confidence interval]: 2.6 [1.2, 4.0] µV; t(814) = 4.66, p < 0.0001). Visual acuities estimated from LCD stimulation significantly underestimated the subjective visual acuity up to 0.2 logMAR using the conversion formula of the first experiment. No statistically significant difference was found when using conversion formulas adjusted for each combination of monitor and contrast.

Conclusions

Based on the results of this study, LCD monitors may substitute CRT monitors for presenting the stimuli for the sweep VEP to objectively estimate visual acuity. Nevertheless, it is advisable to perform a calibration and to collect normative data of healthy volunteers using best-corrected and artificially reduced visual acuity for establishing a conversion formula between sweep VEP outcome and the subjective visual acuity before replacing a CRT with an LCD stimulator.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10633-022-09883-x.

Low-Load Blood Flow Restriction Squat as Conditioning Activity Within a Contrast Training Sequence in High-Level Preadolescent Trampoline Gymnasts

To investigate the effects of implementing low-load blood flow restriction exercises (LL-BFRE) instead of high-load exercises (HL-RE) in a contrast training program on strength and power performance of high-level young gymnasts. Fifteen high-level pre-pubescent trampoline gymnasts (national level, Tanner Stage II, intermediate experience in strength training) were divided into two groups to complete the same structure of a ten-week contrast strength training program differing only in the configuration of the first resistance exercise of the contrast sequence. The LL-BFRE group (n = 7, four girls, 13.9 ± 0.4 y) performed the first resistance exercise of the contrast with LL-BFRE (20%–30% 1RM, perceived pressure of 7 on a scale from 0 to 10). The HL-RE group (n = 8, four girls, 13.8 ± 0.5 y) trained the first resistance exercise of the contrast sequence with moderate-to-high load (60%–85% 1RM). Before and after the training period, isometric mid-thigh pull (IMTP), squat jump (SJ), counter movement jump (CMJ), and drop-jump (DJ) were performed to evaluate the effect of the intervention on strength and power capacities as primary outcomes. Changes in participants’ anthropometric measures, muscle mass, left and right thigh girth, IMTP relative to bodyweight (IMTP-R), eccentric utilization ratio (EUR), and reactive strength index (RSI) were assessed as secondary outcomes. There was no significant interaction (p > 0.05) between group x time in any power and strength outcome, although SJ and EUR showed a trend to significant interaction (p = 0.06 and p = 0.065, respectively). There was an overall effect of time (p < 0.05) in all power and strength variables (CMJ, SJ, EUR, DJ, RSI, IMTP, and IMTP-R). There was a significant interaction in muscle mass (MM) [β = 0.57 kg, 95% CI = (0.15; 0.98), t₁₃ = 2.67, p = 0.019], revealing that participants in LL-BFRE increased their muscle mass (6.6 ± 3.1%) compared to HL-RE (3.6 ± 2.0%). Anthropometric variables did not present any group or interaction effect. However, there was a time effect (p < 0.05). Implementing LL-BFRE in place of HL-RE as a conditioning activity in a contrast training sequence might be equally effective in improving lower-body strength and power in preadolescent trampoline gymnasts.

Central Visual Function and Genotype-Phenotype Correlations in PDE6A-Associated Retinitis Pigmentosa

Purpose

Autosomal recessive retinitis pigmentosa (arRP) can be caused by mutations in the phosphodiesterase 6A (PDE6A) gene. Here, we describe the natural course of disease progression with respect to central retinal function (i.e., visual acuity, contrast sensitivity, and color vision) and establish a detailed genotype-–phenotype correlation.

Methods

Forty-four patients (26 females; mean age ± SD, 43 ± 13 years) with a confirmed genetic diagnosis of PDE6A-associated arRP underwent comprehensive ophthalmological examinations including best-corrected visual acuity (BCVA) with Early Treatment Diabetic Retinopathy Study charts, contrast sensitivity (CS) with Pelli–Robson charts at distances of 3 m and 1 m, and color vision testing using Roth 28-Hue and Panel D-15 saturated color cups.

Results

The most frequently observed variants were c.998+1G>A/p.?, c.304C>A/p.R102S, and c.2053G>A/p.V685M. Central retinal function in patients homozygous for variant c.304C>A/p.R102S was better when compared to patients homozygous for variant c.998+1G>A/p.?, although the former were older at baseline. Central retinal function was similar in patients homozygous for variant c.304C>A/p.R102S and patients heterozygous for variants c.304C>A/p.R102S and c.2053G>A/p.V685M, although the latter were younger at baseline. Annual decline rates in central retinal function were small.

Conclusions

We conclude that the severity of the different disease-causing PDE6A mutations in humans with respect to central visual function may be ranked as follows: c.2053G>A/p.V685M in homozygous state (most severe) > c.998+1G>A/p.? in homozygous state > c.304C>A/p.R102S and c.2053G>A/p.V685M in compound-heterozygous state > c.304C>A/p.R102S in homozygous state (mildest). The assessment of treatment efficacy in interventional trials will remain challenging due to small annual decline rates in central retinal function.

Mitogenome-wise codon usage pattern from comparative analysis of the first mitogenome of Blepharipa sp. (Muga uzifly) with other Oestroid flies

Uziflies (Family: Tachinidae) are dipteran endoparasites of sericigenous insects which cause major economic loss in the silk industry globally. Here, we are presenting the first full mitogenome of Blepharipa sp. (Acc: KY644698, 15,080 bp, A + T = 78.41%), a dipteran parasitoid of Muga silkworm (Antheraea assamensis) found in the Indian states of Assam and Meghalaya. This study has confirmed that Blepharipa sp. mitogenome gene content and arrangement is similar to other Tachinidae and Sarcophagidae flies of Oestroidea superfamily, typical of ancestral Diptera. Although, Calliphoridae and Oestridae flies have undergone tRNA translocation and insertion, forming unique intergenic spacers (IGS) and overlapping regions (OL) and a few of them (IGS, OL) have been conserved across Oestroidea flies. The Tachinidae mitogenomes exhibit more AT content and AT biased codons in their protein-coding genes (PCGs) than the Oestroidea counterpart. About 92.07% of all (3722) codons in PCGs of this new species have A/T in their 3rd codon position. The high proportion of AT and repeats in the control region (CR) affects sequence coverage, resulting in a short CR (Blepharipa sp.: 168 bp) and a smaller tachinid mitogenome. Our research unveils those genes with a high AT content had a reduced effective number of codons, leading to high codon usage bias. The neutrality test shows that natural selection has a stronger influence on codon usage bias than directed mutational pressure. This study also reveals that longer PCGs (e.g., nad5, cox1) have a higher codon usage bias than shorter PCGs (e.g., atp8, nad4l). The divergence rates increase nonlinearly as AT content at the 3rd codon position increases and higher rate of synonymous divergence than nonsynonymous divergence causes strong purifying selection. The phylogenetic analysis explains that Blepharipa sp. is well suited in the family of insectivorous tachinid maggots. It's possible that biased codon usage in the Tachinidae family reduces the effective number of codons, and purifying selection retains the core functions in their mitogenome, which could help with efficient metabolism in their endo-parasitic life style and survival strategy.

Level-specific residuals and diagnostic measures, plots, and tests for random effects selection in multilevel and mixed models

Multilevel data structures are often found in multiple substantive research areas, and multilevel models (MLMs) have been widely used to allow for such multilevel data structures. One important step when applying MLM is the selection of an optimal set of random effects to account for variability and heteroscedasticity in multilevel data. Literature reviews on current practices in applying MLM showed that diagnostic plots are only rarely used for model selection and for model checking. In this study, possible random effects and a generic description of the random effects were provided to guide researchers to select necessary random effects. In addition, based on extensive literature reviews, level-specific diagnostic plots were presented using various kinds of level-specific residuals, and diagnostic measures and statistical tests were suggested to select a set of random effects. Existing and newly proposed methods were illustrated using two data sets: a cross-sectional data set and a longitudinal data set. Along with the illustration, we discuss the methods and provide guidelines to select necessary random effects in model-building steps. R code was provided for the analyses.

Redefining the role of Ca2+-permeable channels in photoreceptor degeneration using diltiazem

Hereditary degeneration of photoreceptors has been linked to over-activation of Ca²⁺-permeable channels, excessive Ca²⁺-influx, and downstream activation of Ca²⁺-dependent calpain-type proteases. Unfortunately, after more than 20 years of pertinent research, unequivocal evidence proving significant and reproducible photoreceptor protection with Ca²⁺-channel blockers is still lacking. Here, we show that both D- and L-cis enantiomers of the anti-hypertensive drug diltiazem were very effective at blocking photoreceptor Ca²⁺-influx, most probably by blocking the pore of Ca²⁺-permeable channels. Yet, unexpectedly, this block neither reduced the activity of calpain-type proteases, nor did it result in photoreceptor protection. Remarkably, application of the L-cis enantiomer of diltiazem even led to a strong increase in photoreceptor cell death. These findings shed doubt on the previously proposed links between Ca²⁺ and retinal degeneration and are highly relevant for future therapy development as they may serve to refocus research efforts towards alternative, Ca²⁺-independent degenerative mechanisms.

Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift

Resurrection studies are a useful tool to measure how phenotypic traits have changed in populations through time. If these trait modifications correlate with the environmental changes that occurred during the time period, it suggests that the phenotypic changes could be a response to selection. Selfing, through its reduction of effective size, could challenge the ability of a population to adapt to environmental changes. Here, we used a resurrection study to test for adaptation in a selfing population of Medicago truncatula, by comparing the genetic composition and flowering times across 22 generations. We found evidence for evolution toward earlier flowering times by about two days and a peculiar genetic structure, typical of highly selfing populations, where some multilocus genotypes (MLGs) are persistent through time. We used the change in frequency of the MLGs through time as a multilocus fitness measure and built a selection gradient that suggests evolution toward earlier flowering times. Yet, a simulation model revealed that the observed change in flowering time could be explained by drift alone, provided the effective size of the population is small enough (<150). These analyses suffer from the difficulty to estimate the effective size in a highly selfing population, where effective recombination is severely reduced.

A two-stage approach for joint modeling of longitudinal measurements and competing risks data

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the "standard and new anti-epileptic drugs" trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

Identifying Differentially Expressed Genes of Zero Inflated Single Cell RNA Sequencing Data Using Mixed Model Score Tests

Single cell RNA sequencing (scRNA-seq) allows quantitative measurement and comparison of gene expression at the resolution of single cells. Ignoring the batch effects and zero inflation of scRNA-seq data, many proposed differentially expressed (DE) methods might generate bias. We propose a method, single cell mixed model score tests (scMMSTs), to efficiently identify DE genes of scRNA-seq data with batch effects using the generalized linear mixed model (GLMM). scMMSTs treat the batch effect as a random effect. For zero inflation, scMMSTs use a weighting strategy to calculate observational weights for counts independently under zero-inflated and zero-truncated distributions. Counts data with calculated weights were subsequently analyzed using weighted GLMMs. The theoretical null distributions of the score statistics were constructed by mixed Chi-square distributions. Intensive simulations and two real datasets were used to compare edgeR-zinbwave, DESeq2-zinbwave, and scMMSTs. Our study demonstrates that scMMSTs, as supplement to standard methods, are advantageous to define DE genes of zero-inflated scRNA-seq data with batch effects.

Genome-Wide Association Analysis of Longitudinal Bone Mineral Content Data From the Iowa Bone Development Study

The foundation for osteoporosis risk is, in part, established during childhood, adolescence, and young adulthood, all periods of development when bone mass is acquired rapidly. The relative quantity of bone mass accrued is influenced by both lifestyle and genetic factors, although the genetic component is not yet well understood. The purpose of this study was to use a genome-wide association (GWA) analysis to discover single nucleotide polymorphisms (SNPs) associated with: (1) the sex-specific hip bone mineral content at approximately the age of 19 when the amount of bone accrued is near its peak; and (2) the sex-specific rate of hip bone mineral content accrual during the adolescent growth spurt. The Iowa Bone Development Study, a longitudinal cohort study exploring bone health in children, adolescents, and young adults was the source of data. From this cohort, n = 364 (190 females, 174 males) participants were included in GWA analyses to address (1) and n = 258 participants (125 females and 133 males) were included in GWA analyses to address (2). Twenty SNPS were detected having p < 1.0 × 10-5. Of most biologic relevance were 2 suggestive SNPs (rs2051756 and rs2866908) detected in an intron of the DKK2 gene through the GWA analysis that explored peak bone mass in females.

A graphical approach to assess the goodness-of-fit of random-effects linear models when the goal is to measure individual benefits of medical treatments in severely ill patients

Background

Two-dimensional personalized medicine (2-PM) models are tools for measuring individual benefits of medical treatments for chronic diseases which have potential applications in personalized medicine. These models assume normality for the distribution of random effects. It is necessary to examine the appropriateness of this assumption. Here, we propose a graphical approach to assessing the goodness-of-fit of 2-PM models with continuous responses.

Methods

We propose benefit quantile-quantile (BQQ) plots which compare the empirical quantiles of individual benefits from a patient sample predicted through an empirical Bayes (EB) approach versus the quantiles of the theoretical distribution of individual benefits derived from the assumption of normality for the random effects. We examine the performance of the approach by conducting a simulation study that compared 2-PM models with non-normal distributions for the random effects versus models with comparable normal distributions. Cramer-von Mises discrepancies were used to quantify the performance of the approach. The approach was illustrated with data from a clinical trial of imipramine for patients with depression.

Results

Simulations showed that BQQ plots were able to capture deviations from the normality assumption for the random effects and did not show any asymmetric deviations from the y = x line when the random effects were normally distributed. For the depression data, the points of the BQQ plot were scattered around closely to the y = x line, without presenting any asymmetric deviations. This implied the adequacy of the normality assumption for the random effects and the goodness-of-fit of the 2-PM model for the imipramine data.

Conclusion

BQQ plots are sensitive to violations of the normality assumption for the random effects, suggesting that the approach is a useful tool for examining the goodness-of-fit of random-effects linear models when the goal is to measure individual treatment benefits.

Brainstem auditory evoked potentials in children with autism spectrum disorder

OBJECTIVES

This study aimed to analyze the neural encoding of verbal and nonverbal stimuli in individuals with autism spectrum disorder using brainstem auditory evoked potentials.

METHODOLOGY

Thirty individuals between 7 and 12 years of age and of both genders participated in this study. Fifteen were diagnosed with autism spectrum disorder, and 15 had typical development. All subjects had normal hearing and no other impairments. An electrophysiological hearing assessment was performed using brainstem auditory evoked potentials with click and speech stimuli.

RESULTS

In the brainstem auditory evoked potentials with click stimuli, the mean wave I latency was longer for the right ear in both groups, and interpeak intervals III-V were greater for the individuals with autism spectrum disorder. For brainstem auditory evoked potentials with speech stimuli, wave V latency was shorter in individuals with autism spectrum disorder.

CONCLUSION

These data suggest that individuals with autism spectrum disorder may have a dysfunction of the central auditory nervous system for nonverbal stimuli and faster neural encoding of the initial part of the verbal stimulus, suggesting hypersensitivity to complex sounds such as speech.

Evaluation of selective attention in patients with misophonia

INTRODUCTION

Misophonia is characterized by the aversion to very selective sounds, which evoke a strong emotional reaction. It has been inferred that misophonia, as well as tinnitus, is associated with hyperconnectivity between auditory and limbic systems. Individuals with bothersome tinnitus may have selective attention impairment, but it has not been demonstrated in case of misophonia yet.

OBJECTIVE

To characterize a sample of misophonic subjects and compare it with two control groups, one with tinnitus individuals (without misophonia) and the other with asymptomatic individuals (without misophonia and without tinnitus), regarding the selective attention.

METHODS

We evaluated 40 normal-hearing participants: 10 with misophonia, 10 with tinnitus (without misophonia) and 20 without tinnitus and without misophonia. In order to evaluate the selective attention, the dichotic sentence identification test was applied in three situations: firstly, the Brazilian Portuguese test was applied. Then, the same test was applied, combined with two competitive sounds: chewing sound (representing a sound that commonly triggers misophonia), and white noise (representing a common type of tinnitus which causes discomfort to patients).

RESULTS

The dichotic sentence identification test with chewing sound, showed that the average of correct responses differed between misophonia and without tinnitus and without misophonia (p=0.027) and between misophonia and tinnitus (without misophonia) (p=0.002), in both cases lower in misophonia. Both, the dichotic sentence identification test alone, and with white noise, failed to show differences in the average of correct responses among the three groups (p≥0.452).

CONCLUSION

The misophonia participants presented a lower percentage of correct responses in the dichotic sentence identification test with chewing sound; suggesting that individuals with misophonia may have selective attention impairment when they are exposed to sounds that trigger this condition.

Candidate Variants for Additive and Interactive Effects on Bioenergy Traits in Switchgrass (Panicum virgatum L.) Identified by Genome-Wide Association Analyses

Switchgrass ( L.) is a promising herbaceous energy crop, but further gains in biomass yield and quality must be achieved to enable a viable bioenergy industry. Developing DNA markers can contribute to such progress, but depiction of genetic bases should be reliable, involving simple additive marker effects and also interactions with genetic backgrounds (e.g., ecotypes) or synergies with other markers. We analyzed plant height, C content, N content, and mineral concentration in a diverse panel consisting of 512 genotypes of upland and lowland ecotypes. We performed association analyses based on exome capture sequencing and tested 439,170 markers for marginal effects, 83,290 markers for marker × ecotype interactions, and up to 311,445 marker pairs for pairwise interactions. Analyses of pairwise interactions focused on subsets of marker pairs preselected on the basis of marginal marker effects, gene ontology annotation, and pairwise marker associations. Our tests identified 12 significant effects. Homology and gene expression information corroborated seven effects and indicated plausible causal pathways: flowering time and lignin synthesis for plant height; plant growth and senescence for C content and mineral concentration. Four pairwise interactions were detected, including three interactions preselected on the basis of pairwise marker correlations. Furthermore, a marker × ecotype interaction and a pairwise interaction were confirmed in an independent switchgrass panel. Our analyses identified reliable candidate variants for important bioenergy traits. Moreover, they exemplified the importance of interactive effects for depicting genetic bases and illustrated the usefulness of preselecting marker pairs for identifying pairwise marker interactions in association studies.

Influence of Maternal Fish Intake on the Anthropometric Indices of Children in the Western Amazon

We studied trends in fish intake among pregnant women living in the Madeira River Basin in Rondônia State, Brazil, to investigate the influence of maternal fish intake on anthropometric indices of children followed up to 5 years. Maternal fish intake was assessed using hair mercury concentrations of mothers and children at delivery and 6, 24, and 59 months. Data analysis was performed using a linear mixed-effect model. Mothers were predominantly young, had low incomes and limited schooling, and breastfed for >6 months. Only 1.9% of children had low birth weight. Anthropometric indices in approximately 80% of the study population showed Z-score values ranging from ≥-2 to ≤1. The influence of maternal fish intake on anthropometric indices, including height-to-age (H/A), weight-to-age (W/A), and weight-to-height (W/H) were not statistically significant after model adjustments. However, higher income and larger birth weight had a positive influence on H/A and W/A, whereas W/H gain was favored by higher maternal educational status and breastfeeding duration. Other variables (hemoglobin concentration and maternal age) had a positive significant influence on anthropometric indices. Maternal fish intake (or its attendant MeHg exposure) did not affect children growth. Nevertheless, it is advisable to avoid mercury-contaminated fish during pregnancy and childhood.

Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML

We review and propose several methods for identifying possible outliers and evaluate their properties. The methods are applied to a genomic prediction program in hybrid rye. Many plant breeders use ANOVA-based software for routine analysis of field trials. These programs may offer specific in-built options for residual analysis that are lacking in current REML software. With the advance of molecular technologies, there is a need to switch to REML-based approaches, but without losing the good features of outlier detection methods that have proven useful in the past. Our aims were to compare the variance component estimates between ANOVA and REML approaches, to scrutinize the outlier detection method of the ANOVA-based package PlabStat and to propose and evaluate alternative procedures for outlier detection. We compared the outputs produced using ANOVA and REML approaches of four published datasets of generalized lattice designs. Five outlier detection methods are explained step by step. Their performance was evaluated by measuring the true positive rate and the false positive rate in a dataset with artificial outliers simulated in several scenarios. An implementation of genomic prediction using an empirical rye multi-environment trial was used to assess the outlier detection methods with respect to the predictive abilities of a mixed model for each method. We provide a detailed explanation of how the PlabStat outlier detection methodology can be translated to REML-based software together with the evaluation of alternative methods to identify outliers. The method combining the Bonferroni-Holm test to judge each residual and the residual standardization strategy of PlabStat exhibited good ability to detect outliers in small and large datasets and under a genomic prediction application. We recommend the use of outlier detection methods as a decision support in the routine data analyses of plant breeding experiments.

Bayesian joint modeling of longitudinal and spatial survival AIDS data

Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for analyzing cancer and AIDS data. As both repeated measurements (longitudinal) and time-to-event (survival) outcomes are observed in an individual, a joint modeling is more appropriate because it takes into account the dependence between the two types of responses, which are often analyzed separately. We propose a Bayesian hierarchical model for jointly modeling longitudinal and survival data considering functional time and spatial frailty effects, respectively. That is, the proposed model deals with non-linear longitudinal effects and spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same region. This joint approach is applied to a cohort study of patients with HIV/AIDS in Brazil during the years 2002-2006. Our Bayesian joint model presents considerable improvements in the estimation of survival times of the Brazilian HIV/AIDS patients when compared with those obtained through a separate survival model and shows that the spatial risk of death is the same across the different Brazilian states. Copyright © 2016 John Wiley & Sons, Ltd.

Model selection and diagnostics for joint modeling of survival and longitudinal data with crossing hazard rate functions

Comparison of two hazard rate functions is important for evaluating treatment effect in studies concerning times to some important events. In practice, it may happen that the two hazard rate functions cross each other at one or more unknown time points, representing temporal changes of the treatment effect. Also, besides survival data, there could be longitudinal data available regarding some time-dependent covariates. When jointly modeling the survival and longitudinal data in such cases, model selection and model diagnostics are especially important to provide reliable statistical analysis of the data, which are lacking in the literature. In this paper, we discuss several criteria for assessing model fit that have been used for model selection and apply them to the joint modeling of survival and longitudinal data for comparing two crossing hazard rate functions. We also propose hypothesis testing and graphical methods for model diagnostics of the proposed joint modeling approach. Our proposed methods are illustrated by a simulation study and by a real-data example concerning two early breast cancer treatments.