Linear modeling of zonal level crop production in Ethiopia

Accounting for zonal-level variations and identifying factors that have linear effects on crop production help to make better decisions and plan new policies for effective crop production and food security. The main objective of this study is to identify potential subsets of covariates and estimate their linear effects on crop production. A linear mixed effects model (random--intercept) is used on agricultural sample survey data for Meher seasons from 2012/13 to 2019/20 to explore and identify the best variance-covariance structure for the longitudinal data on 90 zones with eight repeated observations and different sampling weights. The minimum, mean, and maximum crop production by farmers across the country are 1.616, 8.693, and 147.843 quintals, respectively, and about 98 % of farmers produced less than 25 quintals. There is a small rate of increase in mean and median crop production by farmers across the years, and the variability between zones is highest in the year 2019/20 and in the Somali region. The histogram, kernel density, and P-P plots suggested a common logarithm transformation on the crop production variable. Results from the data exploration and variance-covariance structure selection methods suggested a heterogeneous compound symmetry (CSH) structure. Covariates region, year, proportion of farmers who practice pure-agriculture and other-agriculture types, proportion of farmers who use any type of fertilizer, farmer's age, area used, farmer association crop production, indigenous seed used, improved seed used, UREA fertilizer used, other fertilizers used, and percentage of crop damaged are significant in linearly explaining/affecting log crop production, and among these area used, farmers association crop production, UREA fertilizer used, and indigenous seed used have relatively highest effect on log crop production. Zones Wolayita, North-Shewa (Am), West-Arsi, West-Welega, Dawro, and Guji are top/good performers while zones Southwest-Shewa, Waghimra, Guraghe, South-Omo, Keffa, North-Wello, South-Wello, and Eastern Tigray are bottom/poor performers in crop production. Model assumptions and influence diagnostics results suggested the linearity of the model and normality of random effects and residuals are not violated, even though some zones have influences on either model parameters, precisions of estimates of these parameters, and predicted values.

The effectiveness of an online short-format Recovery College model: a co-learning model to support mental health

BACKGROUND

Our societies are facing mental health challenges, which have been compounded by the Covid-19. This event led people to isolate themselves and to stop seeking the help they needed. In response to this situation, the Health and Recovery Learning Center, applying the Recovery College (RC) model, modified its training program to a shorter online format. This study examines the effectiveness of a single RC training course delivered in a shortened online format to a diverse population at risk of mental health deterioration in the context of Covid-19.

METHODS

This quasi-experimental study used a one-group pretest-posttest design with repeated measures. Three hundred and fifteen (n = 315) learners agreed to take part in the study and completed questionnaires on wellbeing, anxiety, resilience, self-management, empowerment and stigmatizing attitudes and behaviors.

RESULTS

Analyses of variance using a linear mixed models revealed that attending a RC training course had, over time, a statistically significant effect on wellbeing (p = 0.004), anxiety (p < 0.001), self-esteem/self-efficacy (p = 0.005), disclosure/help-seeking (p < 0.001) and a slight effect on resilience (p = 0.019) and optimism/control over the future (p = 0.01).

CONCLUSIONS

This study is the first to measure participation in a single online short-format RC training course, with a diversity of learners and a large sample. These results support the hypothesis that an online short-format training course can reduce psychological distress and increase self-efficacy and help-seeking.

TRIAL REGISTRATION

This study was previously approved by two certified ethics committees: Comité d'éthique de la recherche du CIUSSS EMTL, which acted as the committee responsible for the multicenter study, reference number MP-12-2021-2421, and Comité d'éthique avec les êtres humains de l'UQTR, reference number CER-20-270-07.01.

Performance of Linear Mixed Models in Estimating Structural Rates of Glaucoma Progression Using Varied Random Effect Distributions

Purpose

To compare how linear mixed models (LMMs) using Gaussian, Student t, and log-gamma (LG) random effect distributions estimate rates of structural loss in a glaucomatous population using OCT and to compare model performance to ordinary least squares (OLS) regression.

Design

Retrospective cohort study.

Subjects

Patients in the Bascom Palmer Glaucoma Repository (BPGR).

Methods

Eyes with ≥ 5 reliable peripapillary retinal nerve fiber layer (RNFL) OCT tests over ≥ 2 years were identified from the BPGR. Retinal nerve fiber layer thickness values from each reliable test (signal strength ≥ 7/10) and associated time points were collected. Data were modeled using OLS regression as well as LMMs using different random effect distributions. Predictive modeling involved constructing LMMs with (n - 1) tests to predict the RNFL thickness of subsequent tests. A total of 1200 simulated eyes of different baseline RNFL thickness values and progression rates were developed to evaluate the likelihood of declared progression and predicted rates.

Main Outcome Measures

Model fit assessed by Watanabe-Akaike information criterion (WAIC) and mean absolute error (MAE) when predicting future RNFL thickness values; log-rank test and median time to progression with simulated eyes.

Results

A total of 35 862 OCT scans from 5766 eyes of 3491 subjects were included. The mean follow-up period was 7.0 ± 2.3 years, with an average of 6.2 ± 1.4 tests per eye. The Student t model produced the lowest WAIC. In predictive models, all LMMs demonstrated a significant reduction in MAE when estimating future RNFL thickness values compared with OLS (P < 0.001). Gaussian and Student t models were similar and significantly better than the LG model in estimating future RNFL thickness values (P < 0.001). Simulated eyes confirmed LMM performance in declaring progression sooner than OLS regression among moderate and fast progressors (P < 0.01).

Conclusions

LMMs outperformed conventional approaches for estimating rates of OCT RNFL thickness loss in a glaucomatous population. The Student t model provides the best model fit for estimating rates of change in RNFL thickness, although the use of the Gaussian or Student t distribution in models led to similar improvements in accurately estimating RNFL loss.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Relationship between Intraocular Pressure Fluctuation and Visual Field Progression Rates in the United Kingdom Glaucoma Treatment Study

PURPOSE

To investigate whether intraocular pressure (IOP) fluctuation is associated independently with the rate of visual field (VF) progression in the United Kingdom Glaucoma Treatment Study.

DESIGN

Randomized, double-masked, placebo-controlled multicenter trial.

PARTICIPANTS

Participants with ≥5 VFs (213 placebo, 217 treatment).

METHODS

Associations between IOP metrics and VF progression rates (mean deviation [MD] and five fastest locations) were assessed with linear mixed models. Fluctuation variables were mean Pascal ocular pulse amplitude (OPA), standard deviation (SD) of diurnal Goldmann IOP (diurnal fluctuation), and SD of Goldmann IOP at all visits (long-term fluctuation). Fluctuation values were normalized for mean IOP to make them independent from the mean IOP. Correlated nonfluctuation IOP metrics (baseline, peak, mean, supine, and peak phasing IOP) were combined with principal component analysis, and principal component 1 (PC1) was included as a covariate. Interactions between covariates and time from baseline modeled the effect of the variables on VF rates. Analyses were conducted separately in the two treatment arms.

MAIN OUTCOME MEASURES

Associations between IOP fluctuation metrics and rates of MD and the five fastest test locations.

RESULTS

In the placebo arm, only PC1 was associated significantly with the MD rate (estimate, -0.19 dB/year [standard error (SE), 0.04 dB/year]; P < 0.001), whereas normalized IOP fluctuation metrics were not. No variable was associated significantly with MD rates in the treatment arm. For the fastest five locations in the placebo group, PC1 (estimate, -0.58 dB/year [SE, 0.16 dB/year]; P < 0.001), central corneal thickness (estimate, 0.26 dB/year [SE, 0.10 dB/year] for 10 μm thicker; P = 0.01) and normalized OPA (estimate, -3.50 dB/year [SE, 1.04 dB/year]; P = 0.001) were associated with rates of progression; normalized diurnal and long-term IOP fluctuations were not. In the treatment group, only PC1 (estimate, -0.27 dB/year [SE, 0.12 dB/year]; P = 0.028) was associated with the rates of progression.

CONCLUSIONS

No evidence supports that either diurnal or long-term IOP fluctuation, as measured in clinical practice, are independent factors for glaucoma progression; other aspects of IOP, including mean IOP and peak IOP, may be more informative. Ocular pulse amplitude may be an independent factor for faster glaucoma progression.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The association between mechanical temporal summation, state anxiety at baseline, and persistent low back pain: a 12-month prospective cohort study

BACKGROUND

Persons with acute low back pain (LBP) have a good prognosis for regaining function, while pain often persists. Neurobiological and psychosocial factors are recognized to amplify pain responses, as reported for central sensitization. This study investigated the combination of mechanical temporal summation (TS) chosen to characterize central sensitization and state anxiety representing a psychological factor and their association with persistent pain.

METHODS

A longitudinal prospective cohort study including 176 participants aged between 18 and 65 with acute LBP was performed. The following independent variables were analyzed at baseline: The mechanical TS at the lower back, of whom the Wind-up ratio (WUR) was calculated, and the state anxiety level measured with the State and Trait Anxiety Inventory (STAI-S). The outcome pain intensity was assessed at baseline and 2,3,6 and 12 months after the onset of acute LBP with the Numeric Rating Scale 0-10 (NRS). Linear mixed models (LMM) were used to analyze the association of the independent variables with pain intensity over time.

RESULTS

The mean baseline WUR was 1.3 (SD 0.6) for the right and 1.5 (SD 1.0) for the left side. STAI-S revealed a mean score of 43.1 (SD 5.2). Pain intensity was, on average, 5.4 points (SD 1.6) on the NRS and decreased over one year to a mean of 2.2 (SD 2.4). After one year, 56% of the participants still experienced pain. The LMM revealed a considerable variation, as seen in large confidence intervals. Therefore, associations of the independent variables (WUR and STAI-S) with the course of the outcome pain intensity over one year were not established.

CONCLUSION

This investigation did not reveal an association of mechanical TS and state anxiety at baseline with pain intensity during the one-year measurement period. Pain persistence, mediated by central sensitization, is a complex mechanism that single mechanical TS and state anxiety cannot capture.

Improved Prediction of Perimetric Loss in Glaucomatous Eyes Using Latent Class Mixed Modeling

PURPOSE

To evaluate whether the identification of distinct classes within a population of glaucoma patients improves estimates of future perimetric loss.

DESIGN

Longitudinal cohort study.

PARTICIPANTS

A total of 6558 eyes of 3981 subjects from the Duke Ophthalmic Registry with ≥ 5 reliable standard automated perimetry (SAP) tests and ≥ 2 years of follow-up.

METHODS

Standard automated perimetry mean deviation (MD) values were extracted with associated timepoints. Latent class mixed models (LCMMs) were used to identify distinct subgroups (classes) of eyes according to rates of perimetric change over time. Rates for individual eyes were then estimated by considering both individual eye data and the most probable class membership for that eye. Data were split into training (80%) and test sets (20%), and test set mean squared prediction errors (MSPEs) were estimated using LCMM and ordinary least squares (OLS) regression.

MAIN OUTCOME MEASURES

Rates of change in SAP MD in each class and MSPE.

RESULTS

The dataset contained 52 900 SAP tests with an average of 8.1 ± 3.7 tests per eye. The best-fitting LCMM contained 5 classes with rates of -0.06, -0.21, -0.87, -2.15, and +1.28dB/year (80.0%, 10.2%, 7.5%, 1.3%, and 1.0% of the population, respectively) labeled as slow, moderate, fast, catastrophic progressors, and "improvers" respectively. Fast and catastrophic progressors were older (64.1 ± 13.7 and 63.5 ± 16.9 vs. 57.8 ± 15.8, P < 0.001) and had generally mild-moderate disease at baseline (65.7% and 71% vs. 52%, P < 0.001) than slow progressors. The MSPE was significantly lower for LCMM compared to OLS, regardless of the number of tests used to obtain the rate of change (5.1 ± 0.6 vs. 60.2 ± 37.9, 4.9 ± 0.5 vs. 13.4 ± 3.2, 5.6 ± 0.8 vs. 8.1 ± 1.1, 3.4 ± 0.3 vs. 5.5 ± 1.1 when predicting the fourth, fifth, sixth, and seventh visual fields (VFs) respectively; P < 0.001 for all comparisons). MSPE of fast and catastrophic progressors was significantly lower with LCMM versus OLS (17.7 ± 6.9 vs. 48.1 ± 19.7, 27.1 ± 8.4 vs. 81.3 ± 27.1, 49.0 ± 14.7 vs. 183.9 ± 55.2, 46.6 ± 16.0 vs. 232.4 ± 78.0 when predicting the fourth, fifth, sixth, and seventh VFs respectively; P < 0.001 for all comparisons).

CONCLUSIONS

Latent class mixed model successfully identified distinct classes of progressors within a large glaucoma population that seemed to reflect subgroups observed in clinical practice. Latent class mixed models were superior to OLS regression in predicting future VF observations.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosuremay be found after the references.

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context

Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency.

Objective

The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers' fields in contrasting farming systems worldwide.

Methods

A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time.

Results

Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent.

Conclusion

Big data from farmers' fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space.

Significance

The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Improved genomic prediction using machine learning with Variational Bayesian sparsity

BACKGROUND

Genomic prediction has become a powerful modelling tool for assessing line performance in plant and livestock breeding programmes. Among the genomic prediction modelling approaches, linear based models have proven to provide accurate predictions even when the number of genetic markers exceeds the number of data samples. However, breeding programmes are now compiling data from large numbers of lines and test environments for analyses, rendering these approaches computationally prohibitive. Machine learning (ML) now offers a solution to this problem through the construction of fully connected deep learning architectures and high parallelisation of the predictive task. However, the fully connected nature of these architectures immediately generates an over-parameterisation of the network that needs addressing for efficient and accurate predictions.

RESULTS

In this research we explore the use of an ML architecture governed by variational Bayesian sparsity in its initial layers that we have called VBS-ML. The use of VBS-ML provides a mechanism for feature selection of important markers linked to the trait, immediately reducing the network over-parameterisation. Selected markers then propagate to the remaining fully connected feed-forward components of the ML network to form the final genomic prediction. We illustrated the approach with four large Australian wheat breeding data sets that range from 2665 lines to 10375 lines genotyped across a large set of markers. For all data sets, the use of the VBS-ML architecture improved genomic prediction accuracy over legacy linear based modelling approaches.

CONCLUSIONS

An ML architecture governed under a variational Bayesian paradigm was shown to improve genomic prediction accuracy over legacy modelling approaches. This VBS-ML approach can be used to dramatically decrease the parameter burden on the network and provide a computationally feasible approach for improving genomic prediction conducted with large breeding population numbers and genetic markers.

Tools for photomotor response assay standardization in ecotoxicological studies: Example of exposure to gentamicin in the freshwater planaria Schmidtea mediterranea

Photomotor response assay (PMR) is very useful in an ecotoxicological context because it allows evaluation of behavioral response to potential toxic compounds. However, a lack of procedure standardization makes results comparison difficult between labs and organisms. Here, we aimed to propose five different tools to standardize the PMR procedure so that it may be applied to all model species, regarding: (1) the minimum total sample size, (2) the acclimation period, (3) the number and duration of light and dark phases alternation, (4) the measured behavior, and (5) the statistical analysis. As an example of procedure application, we analyzed the effect of an exposure to the antibiotic gentamicin on the locomotion behavior during PMR in an invertebrate species: the asexual freshwater planaria Schmidtea mediterranea. We encourage future studies using PMR to follow these five tools to improve data analysis and results comparability.

Covid Pandemic Effects on the Physical Fitness of Primary School Children: Results of the German EMOTIKON Project

BACKGROUND

In spring of 2020, the Sars-CoV-2 incidence rate increased rapidly in Germany and around the world. Throughout the next 2 years, schools were temporarily closed and social distancing measures were put in place to slow the spread of the Covid-19 virus. Did these social restrictions and temporary school lockdowns affect children's physical fitness? The EMOTIKON project annually tests the physical fitness of all third-graders in the Federal State of Brandenburg, Germany. The tests assess cardiorespiratory endurance (6-min-run test), coordination (star-run test), speed (20-m sprint test), lower (powerLOW, standing long jump test), and upper (powerUP, ball-push test) limbs muscle power, and static balance (one-legged stance test with eyes closed). A total of 125,893 children were tested in the falls from 2016 to 2022. Primary analyses focused on 98,510 keyage third-graders (i.e., school enrollment according to the legal key date, aged 8 to 9 years) from 515 schools. Secondary analyses included 27,383 older-than-keyage third-graders (i.e., OTK, delayed school enrollment or repetition of a grade, aged 9 to 10 years), who have been shown to exhibit lower physical fitness than expected for their age. Linear mixed models fitted pre-pandemic quadratic secular trends, and took into account differences between children and schools.

RESULTS

Third-graders exhibited lower cardiorespiratory endurance, coordination, speed and powerUP in the Covid pandemic cohorts (2020-2022) compared to the pre-pandemic cohorts (2016-2019). Children's powerLOW and static balance were higher in the pandemic cohorts compared to the pre-pandemic cohorts. From 2020 to 2021, coordination, powerLOW and powerUP further declined. Evidence for some post-pandemic physical fitness catch-up was restricted to powerUP. Cohen's |ds| for comparisons of the pandemic cohorts 2020-2022 with pre-pandemic cohorts 2016-2019 ranged from 0.02 for powerLOW to 0.15 for coordination. Within the pandemic cohorts, keyage children exhibited developmental losses ranging from approximately 1 month for speed to 5 months for cardiorespiratory endurance. For powerLOW and static balance, the positive pandemic effects translate to developmental gains of 1 and 7 months, respectively. Pre-pandemic secular trends may account for some of the observed differences between pandemic and pre-pandemic cohorts, especially in powerLOW, powerUP and static balance. The pandemic further increased developmental delays of OTK children in cardiorespiratory endurance, powerUP and balance.

CONCLUSIONS

The Covid-19 pandemic was associated with declines in several physical fitness components in German third-graders. Pandemic effects are still visible in 2022. Health-related interventions should specifically target those physical fitness components that were negatively affected by the pandemic (cardiorespiratory endurance, coordination, speed).

Are perceptions of climate change in Amazonian coastal communities influenced by socioeconomic and cultural factors?

Climate changes have become undisputed, as have their consequences for global ecosystems and mankind. The coastal areas are among the most affected areas on the planet due to their geographical location. The effects suffered by coastal areas can render the residing populations homeless, as well as compromise the continuity of the history and culture of these environments. The Marine Extractive Reserve of the city of Soure (coastal area of eastern Amazonia) stands out for housing populations that have developed an intimate relationship with nature and have knowledge that can explain people's perception of climate changes. In this context, this study investigated how local residents perceive climate change and its consequences considering different temporal and spatial scales. To this end, questionnaires were developed and applied using a 5-point Likert scale. Our results indicate that perception is shaped by socioeconomic and demographic factors, and that they are perceived on different time scales and geographic space. These findings reflect the awareness-raising efforts of the management body of this Conservation Unit and the local knowledge, derived from the relationship of the residents with the natural environment, which, together, provided the population with assertive information that favor a better understanding of this phenomenon.

Association between cold spells and serum lipid levels among the elders: a distributed-lagged effects analysis

Little evidence about the effects of cold spells on serum lipid levels is available. The aim of this study was to explore the association between cold spells and serum lipid levels among the elders in Jinan, China. Data of old adults from health check-up program in Shandong Provincial Qianfoshan Hospital was collected for this study. Linear mixed models combined with distributed lag nonlinear models were used to examine the relationship between cold spells and serum lipid levels, considering the confounding effects of age, sex, blood pressure, body mass index, and other meteorological factors. Subgroup analysis by gender and analysis based on different definitions of cold spells were also conducted. Increased TG levels in lag 0-lag 2 days and decreased TG levels in lag 5-lag 8 days after cold spells were observed among the elders. The largest increase was 0.363 mmol/L (95% CI: 0.184 ~ 0.543) in lag 0 day, while the largest decreased TG levels was 0.083 mmol/L (95% CI: 0.147 ~ 0.019) in lag 6 day. Similar results were obtained in the analysis of different sex and based on different definitions of cold spells. However, no significant association was found between cold spells with TC, LDL-C, and HDL-C. This study indicates that cold spells were significantly associated with serum TG levels in the elders. Effective preventive measures should be implemented around the cold spells to reduce the volatility of serum lipid levels and the occurrence of subsequent cardiovascular diseases.

A quantified comparison of cortical atlases on the basis of trait morphometricity

BACKGROUND

Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences.

METHODS

Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas.

RESULTS

Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated.

DISCUSSION

Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.

Associations between prenatal exposure to polybrominated diphenyl ethers and physical growth in a seven year cohort study

Although evidence suggests that prenatal exposure to polybrominated diphenyl ethers (PBDEs) alter offspring's physical growth, most studies rely upon physical growth at a single timepoint, and little is known regarding their longitudinal effects over time. In the current study, we determined the associations between prenatal PBDEs exposure and child physical growth by following up 207 mother-child pairs from the Laizhou Wan Birth Cohort (LWBC) from pregnancy until the children were seven years old. Child physical growth including weight, height, and body mass index (BMI) was assessed at birth, and at one, two and seven years of age. Prenatal exposure to PBDEs was quantified by measuring eight PBDE congeners (BDE-28, BDE-47, BDE-85, BDE-99, BDE-100, BDE-153, BDE-154, and BDE-183) in maternal serum samples collected upon hospital admission for delivery. Linear mixed models were applied to examine the associations between prenatal PBDEs exposure and repeated measures of child physical growth, and to determine whether these associations were modified by child's sex. Our findings indicated that BDE-28, BDE-85, BDE-153, BDE-183, and Σ₇PBDEs were positively associated with child weight z-score; and that BDE-28, BDE-47, BDE-85, BDE-99, BDE-153, and Σ₇PBDEs were positively associated with child height z-score. In addition, these associations were modified by the child's sex as reflected by pronounced positive associations among boys, while negative associations were noted among girls. In conclusion, our findings indicated the sex-specific associations between prenatal PBDE exposures and child physical growth during the first seven years of life.

Change point detection for clustered expression data

BACKGROUND

To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points.

RESULTS

In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study.

CONCLUSION

Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.

Atmospheric pollution and mortality in Portugal: Quantitative assessment of the environmental burden of disease using the AirQ+ model

In Portugal, data on mortality rate attributed to household and ambient air pollution are not reported due to shortness and irregularity of the available data series, and therefore, the disclosure of the national progress in reducing the number of deaths and illnesses from air contamination in exposures to multiple pollutants is incomplete. The present work describes the application of the AirQ+ model developed by the WHO to calculate how much of specific health outcomes is attributable to long-term exposure to atmospheric NO₂, PM_2.5, and O₃ in the population of various municipalities in Portugal, from 2010 to 2019. Linear Mixed Models were used for data analysis and have shown that (i) approximately 5000 deaths per year are attributable to exposure to mixtures of NO₂ and PM_2.5; (ii) the spatial distribution of the proportion of deaths attributable to NO₂, PM_2.5 and O₃ shows significant differences between locations, and (iii) that AirQ+ is a useful tool for the purpose of effective Public Health policymaking and reporting on the national progress to implement the 2030 Agenda.

Impact of Mindfulness Training on Spanish Police Officers' Mental and Emotional Health: a Non-Randomized Pilot Study

Objectives

The purpose of this exploratory non-randomized controlled study was to determine the acceptance and effectiveness of an 8-week mindfulness-based intervention (MBI) co-designed by a police officer.

Methods

A pretest-posttest control group design was followed. Participants (MBI group = 20; control group = 18) answered baseline and post-training self-reported measures. In addition, the weekly emotional state of the MBI group was collected. Paired-samples t-test and analysis of covariance were performed for pre-post within-group and between-group differences, respectively, as well as linear mixed effects analysis of repeated measures for week-by-week data.

Results

High acceptance and attendance rates, as well as significant pre-post within-group differences in the MBI group in mindfulness (η² = 0.43), self-compassion (η² = 0.43), depression (η² = 0.54), anxiety (η² = 0.46), stress (η² = 0.51), difficulties in emotion regulation, sleep quality (η² = 0.57), and burnout (η² = 0.31–0.47), were identified. Moreover, police officers who underwent the MBI experienced a week by week decrease of anger, disgust, anxiety, sadness, and desire. Finally, after adjusting for pre-test scores, significant between-group differences were found in the way of attending to internal and external experiences (observing mindfulness facet; η_p² = 0.21), depression symptoms (η_p² = 0.23), general distress (η_p² = 0.24), and the degree of physical and psychological exhaustion (personal burnout; η_p² = 0.20).

Conclusions

The preliminary effectiveness of this MBI on psychopathology and quality of life outcomes in Spanish police officers was discussed. Previous evidence regarding the promising use of MBIs in this population was supported.

Development of a Third Trimester Contingent Prognostic Prediction Scheme for Suspected Early-Onset Pre-Eclampsia

INTRODUCTION

Short-term prediction of pre-eclampsia (PE) using soluble FMS-like tyrosine kinase-1 (sFlt-1)/ placental growth factor (PlGF) ratio has high false-positive rate. Therefore, we developed a prognostic prediction tool that predicts early-onset PE leading to delivery within 1 week on pregnancies with an sFlt-1/PlGF ratio above 38 and compared it with an analogous model based on sFlt-1/PlGF ratio and with the 655 sFlt-1/PlGF ratio cutoff.

METHODS

Cohort study of 363 singleton pregnancies with clinical suspicion of PE before 34 weeks of gestation, allowing repeated assessments (522). 213 samples with an sFlt-1/PlGF ratio above 38 were assessed to construct and identify the best-fit linear mixed model. N-terminal pro-B-type natriuretic peptide (NT-proBNP), sFlt-1 MoM, PlGF MoM, and sFlt-1/PlGF ratio combined with gestational age (GA) were assessed.

RESULTS

None of the pregnancies with an sFlt-1/PlGF ratio of 38 or below developed early-onset PE (309 samples from 240 pregnancies). Conversely, 47 women of 213 assessments (22.1%) with an sFlt-1/PlGF ratio above 38 developed the assessed outcome. The selected model included sFlt-1 MoM, NT-proBNP, and GA. Differences in area under the curve were observed between the selected model and the GA + sFlt-1/PlGF model (p = 0.04). At an sFlt-1/PlGF ratio cutoff of 655, detection rate was 31.9% (15/47), while the selected model detection was 55.3% (26/47) (p = 0.008).

DISCUSSION

Considering repeated assessments, the sFlt-1/PlGF ratio of 38 or below adequately ruled out early-onset PE, leading to delivery within 1 week. However, when sFlt-1/PlGF ratio is above 38, the prediction tool derived from linear mixed model based on GA, NT-proBNP, and sFlt-1 MoM, provided a better prognosis prediction than the sFlt-1/PlGF ratio.

The impact of non-pharmaceutical interventions on COVID-19 epidemic growth in the 37 OECD member states

We estimated the impact of a comprehensive set of non-pharmeceutical interventions on the COVID-19 epidemic growth rate across the 37 member states of the Organisation for Economic Co-operation and Development during the early phase of the COVID-19 pandemic and between October and December 2020. For this task, we conducted a data-driven, longitudinal analysis using a multilevel modelling approach with both maximum likelihood and Bayesian estimation. We found that during the early phase of the epidemic: implementing restrictions on gatherings of more than 100 people, between 11 and 100 people, and 10 people or less was associated with a respective average reduction of 2.58%, 2.78% and 2.81% in the daily growth rate in weekly confirmed cases; requiring closing for some sectors or for all but essential workplaces with an average reduction of 1.51% and 1.78%; requiring closing of some school levels or all school levels with an average reduction of 1.12% or 1.65%; recommending mask wearing with an average reduction of 0.45%, requiring mask wearing country-wide in specific public spaces or in specific geographical areas within the country with an average reduction of 0.44%, requiring mask-wearing country-wide in all public places or all public places where social distancing is not possible with an average reduction of 0.96%; and number of tests per thousand population with an average reduction of 0.02% per unit increase. Between October and December 2020 work closing requirements and testing policy were significant predictors of the epidemic growth rate. These findings provide evidence to support policy decision-making regarding which NPIs to implement to control the spread of the COVID-19 pandemic.

Type I error control for cluster randomized trials under varying small sample structures

Background

Linear mixed models (LMM) are a common approach to analyzing data from cluster randomized trials (CRTs). Inference on parameters can be performed via Wald tests or likelihood ratio tests (LRT), but both approaches may give incorrect Type I error rates in common finite sample settings. The impact of different combinations of cluster size, number of clusters, intraclass correlation coefficient (ICC), and analysis approach on Type I error rates has not been well studied. Reviews of published CRTs find that small sample sizes are not uncommon, so the performance of different inferential approaches in these settings can guide data analysts to the best choices.

Methods

Using a random-intercept LMM stucture, we use simulations to study Type I error rates with the LRT and Wald test with different degrees of freedom (DF) choices across different combinations of cluster size, number of clusters, and ICC.

Results

Our simulations show that the LRT can be anti-conservative when the ICC is large and the number of clusters is small, with the effect most pronouced when the cluster size is relatively large. Wald tests with the between-within DF method or the Satterthwaite DF approximation maintain Type I error control at the stated level, though they are conservative when the number of clusters, the cluster size, and the ICC are small.

Conclusions

Depending on the structure of the CRT, analysts should choose a hypothesis testing approach that will maintain the appropriate Type I error rate for their data. Wald tests with the Satterthwaite DF approximation work well in many circumstances, but in other cases the LRT may have Type I error rates closer to the nominal level.

Genetic variation for tolerance to the downy mildew pathogen Peronospora variabilis in genetic resources of quinoa (Chenopodium quinoa)

BACKGROUND

Quinoa (Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding.

RESULTS

We infected 132 South American genotypes, three Danish cultivars and the weedy relative C. album with a single isolate of P. variabilis under greenhouse conditions and observed a large variation in disease traits like severity of infection, which ranged from 5 to 83%. Linear mixed models revealed a significant effect of genotypes on disease traits with high heritabilities (0.72 to 0.81). Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance.

CONCLUSIONS

The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding.

Dosage-sensitive molecular mechanisms are associated with the tissue-specificity of traits and diseases

Hereditary diseases and complex traits often manifest in specific tissues, whereas their causal genes are expressed in many tissues that remain unaffected. Among the mechanisms that have been suggested for this enigmatic phenomenon is dosage-sensitive compensation by paralogs of causal genes. Accordingly, tissue-selectivity stems from dosage imbalance between causal genes and paralogs that occurs particularly in disease-susceptible tissues. Here, we used a large-scale dataset of thousands of tissue transcriptomes and applied a linear mixed model (LMM) framework to assess this and other dosage-sensitive mechanisms. LMM analysis of 382 hereditary diseases consistently showed evidence for dosage-sensitive compensation by paralogs across diseases subsets and susceptible tissues. LMM analysis of 135 candidate genes that are strongly associated with 16 tissue-selective complex traits revealed a similar tendency among half of the trait-associated genes. This suggests that dosage-sensitive compensation by paralogs affects the tissue-selectivity of complex traits, and can be used to illuminate candidate genes' modes of action. Next, we applied LMM to analyze dosage imbalance between causal genes and three classes of genetic modifiers, including regulatory micro-RNAs, pseudogenes, and genetic interactors. Our results propose modifiers as a fundamental axis in tissue-selectivity of diseases and traits, and demonstrates the power of LMM as a statistical framework for discovering treatment avenues.

Identification of Dysregulated Genes for Late-Onset Alzheimer's Disease Using Gene Expression Data in Brain

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenerative complex brain disease that represents a public health concern. AD is considered the fifth leading cause of death in Americans who are older than 65 years which prioritizes the importance of understanding the etiology of AD in its early stages before the onset of symptoms. This study attempted to further understand Alzheimer's disease (AD) etiology by investigating the dysregulated genes using gene expression data from multiple brain regions.

METHODS

A linear mixed-effects model for differential gene expression analysis was used in a sample of 15 AD and 30 control subjects, each with data from four different brain regions, in order to deal with the hierarchical multilevel data. Post-hoc Gene Ontology and pathway enrichment analyses provided insights on the biological implications in AD progression. Supervised machine learning algorithms were used to assess the discriminative power of the top 10 candidate genes in distinguishing between the two groups.

RESULTS

Enrichment analyses revealed biological processes and pathways that are related to structural constituents and organization of the axons and synapses. These biological processes and pathways imply dysfunctional axon and synaptic transmission between neuronal cells in AD. Random Forest classification algorithm gave the best accuracy on the test data with F1-score of 0.88.

CONCLUSION

The differentially expressed genes were associated with axon and synaptic transmissions which affect the neuronal connectivity in cognitive systems involved in AD pathophysiology. These genes may open ways to explore new effective treatments and early diagnosis before the onset of clinical symptoms.

Determinants of Vitamin B6 Status in Community-Dwelling Older Adults: A Longitudinal Study Over a Period of 18 Years

BACKGROUND

Cross-sectional studies indicate an age-related decline in vitamin B6 status. Because longitudinal studies are lacking, the present study investigates the long-term association between age and vitamin B6 status in older adults by considering potential confounding factors.

METHODS

The study population consists of 249 women and 111 men aged ≥ 60 years, who had at least three follow-ups between 1996 and 2014 with complete data records on relevant parameters. Vitamin B6 status was assessed by serum pyridoxal 5'-phosphate (PLP) concentrations measured by high-performance liquid chromatography. Linear mixed models were used to analyze the influence of age, sex, body composition, supplements, diet, lifestyle, and serum creatinine on PLP concentrations.

RESULTS

At baseline, 37% of the subjects showed PLP concentrations < 30 nmol/L and more than half failed to meet the recommended dietary intake. Longitudinal analyses revealed that age, use of supplements and protein intake were positive determinants of PLP concentrations, whereas body fat showed a negative impact. No influence of sex, dietary vitamin B6 intake, lifestyle factors or serum creatinine on PLP concentrations was found.

CONCLUSION

The present study provides no evidence that in the course of aging PLP concentrations decline between 60 and 90 years. However, age-related changes in body composition, such as an increased ratio of fat mass to fat-free mass may negatively affect vitamin B6 status.

GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure

Background

Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions.

Results

We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates (github.com/wanyuac/GeneMates). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated.

Conclusion

GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data.

Drawing statistical conclusions from experiments with multiple quantitative measurements per subject

In experiments with multiple quantitative measurements per subject, for example measurements on multiple lesions per patient, the additional measurements on the same patient provide limited additional information. Treating these measurements as independent observations will produce biased estimators for standard deviations and confidence intervals, and increases the risk of false positives in statistical tests. The problem can be remedied in a simple way by first taking the average of all observations of each specific patient, and then doing all further calculations only on the list of these patient means. A more sophisticated statistical modeling of the experiment, for example in a linear mixed model, is only required if (i) there is a large imbalance in the number of observations per patient or (ii) there is a specific interest in actually identifying the various sources of variation in the experiment.

Score-based tests for detecting heterogeneity in linear mixed models

Cross-level interactions among fixed effects in linear mixed models (also known as multilevel models) can be complicated by heterogeneity stemming from random effects and residuals. When heterogeneity is present, tests of fixed effects (including cross-level interaction terms) are subject to inflated type I or type II error. While the impact of variance change/heterogeneity has been noticed in the literature, few methods have been proposed to detect this heterogeneity in a simple, systematic way. In addition, when heterogeneity among clusters is detected, researchers often wish to know which clusters' variances differed from the others. In this study, we utilize a recently proposed family of score-based tests to distinguish between cross-level interactions and heterogeneity in variance components, also providing information about specific clusters that exhibit heterogeneity. These score-based tests only require estimation of the null model (when variance homogeneity is assumed to hold), and they have been previously applied to psychometric models to detect measurement invariance. In this paper, we extend the tests to linear mixed models, allowing us to use the tests to differentiate between interaction and heterogeneity. We detail the tests' implementation and performance via simulation, provide an empirical example of the tests' use in practice, and provide code illustrating the tests' general application.

Fasciola hepatica seroprevalence in Northern German dairy herds and associations with milk production parameters and milk ketone bodies

Infections with the liver fluke Fasciola hepatica remain a serious problem in dairy herds causing significant production losses. In sheep, a strong relationship between F. hepatica infections and an increase in serum ketone bodies due to reduced feed intake and liver damage was demonstrated. We hypothesized that F. hepatica infections might contribute to an increase in milk ketone bodies in dairy herds. Thus, the objective of the study was to estimate the association between F. hepatica bulk tank milk (BTM) antibodies and milk production parameters (milk yield, milk protein, fat yield), somatic cell count (SCC) and the milk ketone bodies ß-hydroxybutyrate (BHB) and acetone, inferred from Fourier transform infrared (FTIR) spectrometry, via linear mixed model analysis. A further aim was to follow up the F. hepatica seroprevalence in dairy herds in the northern German region East Frisia. We collected BTM samples between October and December from 1022 herds in 2017 and 1318 herds in 2018. Overall, 33.1 % of the herds tested positive in 2017 and 37.0 % in 2018, showing decreased F. hepatica seroprevalences compared to prior seroprevalence studies in the same region in 2010, 2008 and 2006 (> 45 % positive herds). We estimated a significant negative association (P < 0.001) between herd F. hepatica infection category and average milk yield with a loss of -1.62 kg per cow per day in strongly infected herds compared to BTM ELISA negative herds. Moreover, F. hepatica infection category had a significant effect on herd average milk protein and fat yield (P < 0.001), showing a decrease of 0.06 kg for both parameters from BTM ELISA negative herds to strongly infected herds. No significant association with milk SCC was found (P = 0.664). Regarding ketone bodies, we estimated significant higher average BHB values in strongly infected herds compared to the other three infection categories in the model analysis (P = 0.002). The association between F. hepatica infection category and acetone values was not significant (P = 0.079). Besides primary ketosis, fasciolosis should be considered as differential diagnosis in dairy herds with increased BHB values.

Modelling of population structure through contemporary groups in genetic evaluation

Background

Forest trees can occupy extensive geography and environmentally highly variable areas which result in high genetic variability in the direction of pressure from natural selection. At the same time, the majority of conifer species are wind-pollinated from both short and long distances, resulting in wide-spread gene flow, which can lead to maladaptation to local conditions. Quantitative analyses of provenance/progeny tests correct for genetic differences between populations to ensure unbiased genetic parameters are obtained. Commonly, the provenance effect is fitted as a fixed term or can be implemented as a contemporary group in the pedigree.

Results

The use of a provenance effect, either as a fixed term or as the same contemporary groups in both maternal and paternal sides of the pedigree, resulted in fairly similar precision of genetic parameters in our case. However, when we developed a phantom contemporary group for the paternal side of the pedigree that considered a different genetic quality of pollen compared with the maternal contribution from trees in the local environment, the model fit and accuracy of breeding values increased.

Conclusion

Consideration of the mating dynamics and the vector of gene flow are important factors in modelling contemporary genetic groups, particularly when implementing pedigrees within a mixed model framework to obtain unbiased estimates of genetic parameters. This approach is especially important in traits involved in local adaptation.

The design, analysis and application of mouse clinical trials in oncology drug development

Background

Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs.

Methods

We systematically analyzed tumor growth data from a large collection of PDX, CDX and syngeneic mouse tumor models to evaluate multiple efficacy end points, and to introduce statistical methods for modeling MCTs.

Results

We established empirical quantitative relationships between mouse number and measurement accuracy for categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between methods on calling drug responses as objective response. We then introduced linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring drug’s mechanisms of action. We introduced additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered mouse population. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that power is close for designs with similar total number of mice. Finally, we showed that MCTs can explain discrepant results in clinical trials.

Conclusions

Methods proposed in this study can make the design and analysis of MCTs more rational, flexible and powerful, make MCTs a better tool in oncology research and drug development.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5907-7) contains supplementary material, which is available to authorized users.

Comparisons of statistical models for growth curves from 90-day rat feeding studies

The objective of this work was to compare several models of body weight data from 90-day rodent feeding trials. Polynomial and nonlinear functions relating time and weight were examined as were the use of Toeplitz error covariance structures and random coefficients. The models were evaluated by fitting them to five publicly available datasets from rat feeding studies. Model performance was assessed in terms of their ability to capture the complexity of the growth patterns, validity of necessary assumptions, and information criteria scores. The results demonstrated the importance of selecting a curve function that effectively reflects the mean response. Toeplitz error covariance structures resulted in superior model fit, while failing to address deviations from model assumptions. Models using the Richards function and random coefficients were generally superior to the other models evaluated and dramatically improved upon linear models with complex error structures.

Analysis of 11 trace elements in flight feathers of Italian Sparrows in southern Italy: A study of bioaccumulation through age classes, variability in three years of sampling, and relations with body condition

Trace elements have been acknowledged as one of the subtlest environmental hazards in all compartments of the total environment. Enhanced by activities in the anthroposphere, they accumulate in the atmosphere, lithosphere, and hydrosphere. Eventually, trace elements can bioaccumulate or biomagnifiy in the biosphere, with harmful effects on animals occupying higher trophic levels, including humans. Accordingly, there is great interest in assessing and monitoring trace element concentrations in the biosphere, and birds, especially passerines, have been commonly chosen as biomonitors. In this study, the concentration of 11 trace elements was measured (i.e. aluminum, chromium, manganese, iron, nickel, copper, zinc, arsenic, cadmium, barium, and lead) in flight feathers of Italian Sparrows, a common bird species hitherto not analysed in this respect. Samples were collected in an agricultural area in southern Italy, where a mosaic of natural environments, urbanized areas and industrial facilities can be found. Linear mixed modelling was applied to the analysis of flight feathers in juveniles, juvenile birds moulting to adulthood, and adults in three sampling years on 184 birds. Results are timely as they add new data to the scarce available information on Ba and As in bird feathers and showed clear bioaccumulation patterns from juveniles to adults for As, Cr, and Cd. Moreover, the modelling approach showed that the concentration of elements such as As, Cd and Cr can be variable across the years and that some elements, notably Cd and Ba, were inversely correlated with body mass and wing length, respectively, suggesting potential negative effects on bird health. Finally, when modelling bird body condition and trace elements, results showed that Cd and Ba negatively affect birds regardless of age or sampling year. Thus, the Italian Sparrow could be considered as a valuable biomonitor for trace elements in the total environment, especially for Cd and Ba.

Exploring individual differences in task switching

Previous research has shown that there are significant task-switching costs even when participants have time to prepare for task switching after cueing. We investigated individual differences in task switching by monitoring errors and response times of individual participants. In Experiment 1A, 58 participants were encouraged to finish the session early by completing 200 consecutive trials without making an error. In case of a mistake, they had to repeat their effort until the experimental session expired. Using this demanding procedure, 16 participants managed to complete early. Among these 16 we identified 9 best performers who showed no significant switch costs. We conducted follow-up Experiment 1B on these best performers by systematically varying cue-stimulus intervals and inter-trial intervals. The results confirmed that these participants had no significant RT and ER switch costs when they had time to prepare the task between cue and target onset. However, significant switch costs emerged when cue and target stimulus were presented simultaneously. In Experiment 1C, using three classical task-switching paradigms, we compared the best performers with 9 controls who had made frequent errors in Experiment 1A. Although the best performers responded faster and made fewer errors, they only showed reduced switch costs in a pre-cued paradigm that had been extensively practiced. In two other paradigms with simultaneous presentation of cue and target stimulus, best performers had switch costs and showed considerable individual differences similar to the controls. We conclude that there are considerable individual differences in task switching and that smaller individual switch costs are mainly related to efficient task preparation. We speculate that efficient task preparation may be linked to better executive control and general intelligence.

Relationship between caffeine intake and autosomal dominant polycystic kidney disease progression: a retrospective analysis using the CRISP cohort

Background

Caffeine has been proposed, based on in vitro cultured cell studies, to accelerate progression of autosomal dominant polycystic kidney disease (ADPKD) by increasing kidney size. Since ADPKD patients are advised to minimize caffeine intake, we investigated the effect of caffeine on disease progression in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP), a prospective, observational cohort study.

Methods

Our study included 239 patients (mean age = 32.3 ± 8.9 ys; 188 caffeine consumers) with a median follow-up time of 12.5 years. Caffeine intake reported at baseline was dichotomized (any vs. none). Linear mixed models, unadjusted and adjusted for age, race, sex, BMI, smoking, hypertension, genetics and time, were used to model height-adjusted total kidney volume (htTKV) and iothalamate clearance (mGFR). Cox proportional hazards models and Kaplan-Meier plots examined the effect of caffeine on time to ESRD or death.

Results

Caffeine-by-time was statistically significant when modeling ln(htTKV) in unadjusted and adjusted models (p <  0.01) indicating that caffeine consumers had slightly faster kidney growth (by 0.6% per year), but htTKV remained smaller from baseline throughout the study. Caffeine consumption was not associated with a difference in mGFR, or in the time to ESRD or death (p > 0.05). Moreover the results were similar when outcomes were modeled as a function of caffeine dose.

Conclusion

We conclude that caffeine does not have a significant detrimental effect on disease progression in ADPKD.

Electronic supplementary material

The online version of this article (10.1186/s12882-018-1182-0) contains supplementary material, which is available to authorized users.

Global and Regional Trends of Multiple Sclerosis Disability-Adjusted Life Years Rates: A 25-Year Assessment

BACKGROUND

Multiple sclerosis (MS) burden of disease has been described by reporting the disability-adjusted life years (DALY) index. So far, no study has assessed the trend of MS DALY rates over time.

METHOD

Age-standardized MS DALY rates for both sexes were reported every 5 years from 1990 to 2015 in 195 countries in the Global Burden of Disease Database (GBD) database. To assess the MS DALY rates' trends in each super region and throughout the world, we applied the Latent Growth Models. We also utilized the linear mixed model to evaluate the effect of development factor on MS DALY rates.

RESULTS

Our results showed that 5 out of 7 GBD super regions had negative trends in MS DALY rates during these years and the remaining 2 - Latin America and the Caribbean (slope = 0.196, p < 0.05) and South Asia - slope = 0.057, p > 0.05 - had upward trends. Using a linear mixed model, we found that the mean difference of MS DALY rates was about 25 DALYs higher in developed countries compared to developing ones (p < 0.0001).

CONCLUSION

In general, our findings revealed a global downward trend in the MS DALY rate. We also conclude that MS DALY rates are decreasing both in developed and developing countries, with a steeper slope in the developed world.

Bayesian data analysis in the phonetic sciences: A tutorial introduction

This tutorial analyzes voice onset time (VOT) data from Dongbei (Northeastern) Mandarin Chinese and North American English to demonstrate how Bayesian linear mixed models can be fit using the programming language Stan via the R package brms. Through this case study, we demonstrate some of the advantages of the Bayesian framework: researchers can (i) flexibly define the underlying process that they believe to have generated the data; (ii) obtain direct information regarding the uncertainty about the parameter that relates the data to the theoretical question being studied; and (iii) incorporate prior knowledge into the analysis. Getting started with Bayesian modeling can be challenging, especially when one is trying to model one's own (often unique) data. It is difficult to see how one can apply general principles described in textbooks to one's own specific research problem. We address this barrier to using Bayesian methods by providing three detailed examples, with source code to allow easy reproducibility. The examples presented are intended to give the reader a flavor of the process of model-fitting; suggestions for further study are also provided. All data and code are available from: https://osf.io/g4zpv.

An adaptive gene-level association test for pedigree data

Background

We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient.

Results

We applied our approach to test for association with the high-density lipoprotein (HDL) ratio of post- and pretreatments in GAW20 data. Using the LMM similar to that used by Aslibekyan et al. (PLos One, 7:48663, 2012), our method identified 2 nearly significant genes (APOA5 and ZNF259) near rs964184, whereas neither the other gene-level tests nor the standard test on each individual single-nucleotide polymorphism (SNP) detected any significant gene in a genome-wide scan.

Conclusions

Gene-level association testing can be a complementary approach to the SNP-level association testing and our method is adaptive and efficient compared to several other existing gene-level association tests.

Association analyses of repeated measures on triglyceride and high-density lipoprotein levels: insights from GAW20

BACKGROUND

The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided "real" data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set.

RESULTS

The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of statistical methods such as linear mixed models, generalized estimating equations, and quasi-likelihood-based regression models. This article discusses the varying strategies explored by the group's investigators with the common goal of improving the power to detect association with repeated measures of a phenotype.

CONCLUSIONS

Although it is difficult to identify a common message emanating from the different contributions because of the diversity in the issues addressed, the unifying theme of the contributions lie in the search for novel analytic strategies to circumvent the limitations of existing methodologies to detect genetic association.

A D-vine copula-based model for repeated measurements extending linear mixed models with homogeneous correlation structure

We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D-vine copula. We show that our approach is an extremely flexible extension of the widely used linear mixed model if the correlation is homogeneous over the considered individuals. As an alternative to joint maximum-likelihood a sequential estimation approach for the D-vine copula is provided and validated in a simulation study. The model can handle missing values without being forced to discard data. Since conditional distributions are known analytically, we easily make predictions for future events. For model selection, we adjust the Bayesian information criterion to our situation. In an application to heart surgery data our model performs clearly better than competing linear mixed models.

Scarce evidence of ozone effect on recent health and productivity of alpine forests-a case study in Trentino, N. Italy

We investigated the significance of tropospheric ozone as a factor explaining recent tree health (in terms of defoliation) and productivity (in terms of basal area increment, BAI) in 15 ICP Forests level I and one level II plots in alpine forests in Trentino (N. Italy). Mean daily ozone summer concentrations varied between 30 and 72 parts per billion (ppb) leading to large exceedance of concentration-based critical levels set to protect forest trees. Phytoxic ozone dose (POD₀) estimated at the level II plot over the period 1996-2009 was 31-61 mmol m^-2 projected leaf area (PLA). The role of ozone was investigated taking into account other site and environmental factors. Simple linear regression, multiple linear regression (MLR, to study mean periodical defoliation and mean periodical BAI), and linear mixed models (LMM, to study annual defoliation data) were used. Our findings suggest that-regardless of the metric adopted-tropospheric ozone is not a significant factor in explaining recent status and trends of defoliation and BAI in the alpine region examined. Both defoliation and BAI are in turn driven by biotic/abiotic damage, nutritional status, DBH (assumed as a proxy for age), and site characteristics. These results contrast with available ozone-growth dose response relationships (DRRs) and other observational studies. This may be due to a variety of concurrent reasons: (i) DRRs developed for individual saplings under controlled condition are not necessarily valid for population of mature trees into real forest ecosystems; (ii) some observational studies may have suffered from biased design; and (iii) since alpine forests have been exposed to high ozone levels (and other oxidative stress) over decades, possible acclimation mechanisms cannot be excluded.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

BACKGROUND

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.

RESULTS

To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.

CONCLUSIONS

Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Group-Level EEG-Processing Pipeline for Flexible Single Trial-Based Analyses Including Linear Mixed Models

Here we present an application of an EEG processing pipeline customizing EEGLAB and FieldTrip functions, specifically optimized to flexibly analyze EEG data based on single trial information. The key component of our approach is to create a comprehensive 3-D EEG data structure including all trials and all participants maintaining the original order of recording. This allows straightforward access to subsets of the data based on any information available in a behavioral data structure matched with the EEG data (experimental conditions, but also performance indicators, such accuracy or RTs of single trials). In the present study we exploit this structure to compute linear mixed models (LMMs, using lmer in R) including random intercepts and slopes for items. This information can easily be read out from the matched behavioral data, whereas it might not be accessible in traditional ERP approaches without substantial effort. We further provide easily adaptable scripts for performing cluster-based permutation tests (as implemented in FieldTrip), as a more robust alternative to traditional omnibus ANOVAs. Our approach is particularly advantageous for data with parametric within-subject covariates (e.g., performance) and/or multiple complex stimuli (such as words, faces or objects) that vary in features affecting cognitive processes and ERPs (such as word frequency, salience or familiarity), which are sometimes hard to control experimentally or might themselves constitute variables of interest. The present dataset was recorded from 40 participants who performed a visual search task on previously unfamiliar objects, presented either visually intact or blurred. MATLAB as well as R scripts are provided that can be adapted to different datasets.

Real-Time Deformability Cytometry: Label-Free Functional Characterization of Cells

Real-time deformability cytometry (RT-DC) is a microfluidic technique that allows to capture and evaluate morphology and rheology of up to 1000 cells/s in a constricted channel. The cells are deformed without mechanical contact by hydrodynamic forces and are quantified in real-time without the need of additional handling or staining procedures. Segmented pictures of the cells are stored and can be used for further analysis. RT-DC is sensitive to alterations of the cytoskeleton, which allows, e.g., to show differences in cell cycle phases, identify different subpopulations in whole blood and to study mechanical stiffening of cells entering a dormant state. The abundance of the obtainable parameters and the interpretation as mechanical readout is an analytical challenge that needs standardization. Here, we will provide guidelines for measuring and post-processing of RT-DC data.

iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-of-the-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple between- and within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance. Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.

Modelling drivers and distribution of lead and zinc concentrations in soils of an urban catchment (Sydney estuary, Australia)

The human population is increasing globally and land use is changing to accommodate for this growth. Soils within urban areas require closer attention as the higher population density increases the chance of human exposure to urban contaminants. One such example of an urban area undergoing an increase in population density is Sydney, Australia. The city also possesses a notable history of intense industrial activity. By integrating multiple soil surveys and covariates into a linear mixed model, it was possible to determine the main drivers and map the distribution of lead and zinc concentrations within the Sydney estuary catchment. The main drivers as derived from the model included elevation, distance to main roads, main road type, soil landscape, population density (lead only) and land use (zinc only). Lead concentrations predicted using the model exceeded the established guideline value of 300mgkg^-1 over a large portion of the study area with concentrations exceeding 1000mgkg^-1 in the south of the catchment. Predicted zinc did not exceed the established guideline value of 7400mgkg^-1; however concentrations were higher to the south and west of the study area. Unlike many other studies we considered the prediction uncertainty when assessing the contamination risk. Although the predictions indicate contamination over a large area, the broadness of the prediction intervals suggests that in many of these areas we cannot be sure that the site is contaminated. More samples are required to determine the contaminant distribution with greater precision, especially in residential areas where contamination was highest. Managing sources and addressing areas of elevated lead and zinc concentrations in urban areas has the potential to reduce the impact of past human activities and improve the urban environment of the future.

Multiple phenotype association tests using summary statistics in genome-wide association studies

We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.

Fixation durations in scene viewing: Modeling the effects of local image features, oculomotor parameters, and task

Scene perception requires the orchestration of image- and task-related processes with oculomotor constraints. The present study was designed to investigate how these factors influence how long the eyes remain fixated on a given location. Linear mixed models (LMMs) were used to test whether local image statistics (including luminance, luminance contrast, edge density, visual clutter, and the number of homogeneous segments), calculated for 1° circular regions around fixation locations, modulate fixation durations, and how these effects depend on task-related control. Fixation durations and locations were recorded from 72 participants, each viewing 135 scenes under three different viewing instructions (memorization, preference judgment, and search). Along with the image-related predictors, the LMMs simultaneously considered a number of oculomotor and spatiotemporal covariates, including the amplitudes of the previous and next saccades, and viewing time. As a key finding, the local image features around the current fixation predicted this fixation’s duration. For instance, greater luminance was associated with shorter fixation durations. Such immediacy effects were found for all three viewing tasks. Moreover, in the memorization and preference tasks, some evidence for successor effects emerged, such that some image characteristics of the upcoming location influenced how long the eyes stayed at the current location. In contrast, in the search task, scene processing was not distributed across fixation durations within the visual span. The LMM-based framework of analysis, applied to the control of fixation durations in scenes, suggests important constraints for models of scene perception and search, and for visual attention in general.

An efficient linear mixed model framework for meta-analytic association studies across multiple contexts

Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale.

Evaluating the effect of synchronized sea lice treatments in Chile

The sea louse is considered an important ectoparasite that affects farmed salmonids around the world. Sea lice control relies heavily on pharmacological treatments in several salmon-producing countries, including Chile. Among options for drug administration, immersion treatments represent the majority of antiparasitic control strategies used in Chile. As a topical procedure, immersion treatments do not induce a long lasting effect; therefore, re-infestation from neighbouring farms may undermine their efficacy. Synchronization of treatments has been proposed as a strategy to improve immersion treatment performance, but it has not been evaluated so far. Using a repeated-measures linear mixed-effect model, we evaluated the impact of treatment synchronization of neighbouring farms (within 10km seaway distance) on the adult lice mean abundance from weeks 2 to 8 post-treatment on rainbow trout and Atlantic salmon farms in Chile, while controlling for external and internal sources of lice before the treatments, and also for environmental and fish-related variables. Results indicate that treatment synchronization was significantly associated with lower adult lice levels from weeks 5 to 7 after treatment. This relationship appeared to be linear, suggesting that higher levels of synchronization may result in lower adult sea lice levels during these weeks. These findings suggest that synchronization can improve the performance of immersion delousing treatments by keeping sea lice levels low for a longer period of time. Our results may be applicable to other regions of the world where immersion treatments are widely used.

Functional Mixed Effects Model for Small Area Estimation

Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area level data, and fit a varying coefficient linear mixed effect model where the varying coefficients are semi-parametrically modeled via B-splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors, and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.