scholarly journals Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes

2017 ◽  
Author(s):  
Megan M. Herting ◽  
Cory Johnson ◽  
Kathryn L. Mills ◽  
Nandita Vijayakumar ◽  
Meg Dennison ◽  
...  
Keyword(s):  
Sex Differences ◽  
Linear Models ◽  
Developmental Trajectories ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Brain Volumes ◽  
University Of Pittsburgh ◽  
Subcortical Brain ◽  
Males And Females ◽  
University Of Oslo

AbstractThe developmental patterns of subcortical brain volumes in males and females observed in previous studies have been inconsistent. To help resolve these discrepancies, we examined developmental trajectories using three independent longitudinal samples of participants in the age-span of 8-22 years (total 216 participants and 467 scans). These datasets, including Pittsburgh (PIT; University of Pittsburgh, USA), NeuroCognitive Development (NCD; University of Oslo, Norway), and Orygen Adolescent Development Study (OADS; The University of Melbourne, Australia), span three countries and were analyzed together and in parallel using mixed-effects modeling with both generalized additive models and general linear models. For all regions and across all samples, males were found to have significantly larger volumes as compared to females, and significant sex differences were seen in age trajectories over time. However, direct comparison of sample trajectories and sex differences identified within samples were not consistent. The trajectories for the amygdala, putamen, and nucleus accumbens were most consistent between the three samples. Our results suggest that even after using similar preprocessing and analytic techniques, additional factors, such as image acquisition or sample composition may contribute to some of the discrepancies in sex specific patterns in subcortical brain changes across adolescence, and highlight region-specific variations in congruency of developmental trajectories.

scholarly journals Sex differences in the non-linear association between BMI and LDL cholesterol in middle-aged and older adults: findings from two nationally representative surveys in China

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Haibin Li ◽  
Jiahui Ma ◽  
Deqiang Zheng ◽  
Xia Li ◽  
Xiuhua Guo ◽  
...  
Keyword(s):  
Older Adults ◽  
Sex Differences ◽  
Smoking Status ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Middle Aged ◽  
Linear Association ◽  
Non Linear ◽  
Males And Females ◽  
Nationally Representative

Abstract Background The relationship between body mass index (BMI) and low-density lipoprotein cholesterol (LDL-C) has not been clearly elucidated in middle-aged and older adults. This study aimed to evaluate the non-linear dose-response relationship between BMI and LDL-C in males and females. Methods Data was obtained from two nationally representative surveys in China—the China Health and Nutrition Survey (CHNS, 2009) and China Health and Retirement Longitudinal Study (CHARLS, 2011–2012). To evaluate the sex differences in the association between BMI and LDL-C, the generalized additive models with a smooth function for continuous BMI and smooth-factor interaction for sexes with BMI were used. Segmented regressions were fitted to calculate the slopes with different estimated breakpoints among females and males. Results A total of 12,273 participants (47.1% male) aged 45 to 75 years were included. The generalized additive models revealed that a non-linear relationship between BMI and LDL-C level in both sexes after adjustment for age, residence, education levels, marital status, drinking, smoking status, and cohort (CHNS or CHARLS). Slopes of the association between BMI and LDL-C association changed at BMI 20.3 kg/m2 (95% CI: 18.8 to 21.8) in females and 27.1 kg/m2 (95% CI: 25. 8 to 28.4) in males. Below these BMI breakpoints, LDL-C levels increased 1.84 (95% CI: 1.45 to 2.31) in males and 3.49 (95% CI: 1.54 to 5.45) mg/dL per kg/m2 in females. However, LDL-C levels declined − 1.50 (95% CI: − 2.92 to − 0.09) mg/dL per kg/m2 above BMI of 27.1 kg/m2 in males. The non-linear association BMI and LDL-C in males and females was varied by cohort source, age groups, and the number of metabolic syndrome criteria. Conclusions In the Chinese middle aged and older adults, the BMI and LDL-C relationship was inverted U-shaped with a high level of LDL-C at a BMI of 27.1 kg/m2 in males, and an approximately linear association was observed in females.

scholarly journals The Evolution of Data Sharing Practices in the Psychological Literature

2021 ◽  
Author(s):  
Judith Neve ◽  
Guillaume A Rousselet
Keyword(s):  
Data Sharing ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Future Research ◽  
Science Data ◽  
Editorial Policies ◽  
Psychological Science ◽  
The Impact

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

scholarly journals An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets

2011 ◽  
Vol 68 (10) ◽  
pp. 2252-2263 ◽  
Author(s):  
Stéphanie Mahévas ◽  
Youen Vermard ◽  
Trevor Hutton ◽  
Ane Iriondo ◽  
Angélique Jadaud ◽  
...  
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Technical Information ◽  
Fishing Effort ◽  
Additive Models ◽  
Induced Variation ◽  
Fishing Fleets ◽  
The Impact ◽  
Selection Of ◽  
Experience Effect

Abstract Mahévas, S., Vermard, Y., Hutton, T., Iriondo, A., Jadaud, A., Maravelias, C. D., Punzón, A., Sacchi, J., Tidd, A., Tsitsika, E., Marchal, P., Goascoz, N., Mortreux, S., and Roos, D. 2011. An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets. – ICES Journal of Marine Science, 68: 2252–2263. The impact of the fishing effort exerted by a vessel on a population depends on catchability, which depends on population accessibility and fishing power. The work investigated whether the variation in fishing power could be the result of the technical characteristics of a vessel and/or its gear or whether it is a reflection of inter-vessel differences not accounted for by the technical attributes. These inter-vessel differences could be indicative of a skipper/crew experience effect. To improve understanding of the relationships, landings per unit effort (lpue) from logbooks and technical information on vessels and gears (collected during interviews) were used to identify variables that explained variations in fishing power. The analysis was undertaken by applying a combination of generalized additive models and generalized linear models to data from several European fleets. The study highlights the fact that taking into account information that is not routinely collected, e.g. length of headline, weight of otter boards, or type of groundrope, will significantly improve the modelled relationships between lpue and the variables that measure relative fishing power. The magnitude of the skipper/crew experience effect was weaker than the technical effect of the vessel and/or its gear.

Climate Influences on Meningitis Incidence in Northwest Nigeria

2014 ◽  
Vol 6 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Auwal F. Abdussalam ◽  
Andrew J. Monaghan ◽  
Vanja M. Dukić ◽  
Mary H. Hayden ◽  
Thomas M. Hopson ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Skill Score ◽  
Additive Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Explanatory Variables ◽  
Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

scholarly journals Percolation models of pathogen spillover

2019 ◽  
Vol 374 (1782) ◽  
pp. 20180331 ◽  
Author(s):  
Alex D. Washburne ◽  
Daniel E. Crowley ◽  
Daniel J. Becker ◽  
Kezia R. Manlove ◽  
Marissa L. Childs ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Mechanistic Model ◽  
Piecewise Linear ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Management Intervention ◽  
Management Concepts ◽  
The Impact ◽  
Reservoir Hosts

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

Generalized additive models reveal among-stand variation in live tree biomass equations

2020 ◽  
Author(s):  
Jacob I. Levine ◽  
Perry de Valpine ◽  
John J. Battles
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Carbon Accounting ◽  
Stand Age ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Tree Biomass ◽  
Live Tree

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted for sources of variation warrant a re-examination of allometric relationships using modern statistical techniques. We asked the following questions: (1) Is there among-stand variation in allometric relationships? (2) Is there nonlinearity in allometric relationships? (3) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (4) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities which are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results point to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live-tree biomass estimation.

540 Age Trends in Sleep Across the Lifespan: Findings from the Pittsburgh Lifespan Sleep Databank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A213-A213
Author(s):  
Meredith Wallace ◽  
Nicholas Kissel ◽  
Martica Hall ◽  
Anne Germain ◽  
Karen Matthews ◽  
...  
Keyword(s):  
Sex Differences ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Additive Models ◽  
Self Report ◽  
University Of Pittsburgh ◽  
Human Lifespan ◽  
Age Related ◽  
Age Trends ◽  
Age Related Changes

Abstract Introduction Sleep continuously changes over the human lifespan and it does so across multiple dimensions, including duration, timing, efficiency, and variability. Although studies focused on specific developmental periods have shown age-related changes in sleep, methodological differences make it difficult to synthesize information across studies to fully understand precisely when these sleep changes occur. Our goal was to use individual-level actigraphy and self-report sleep data from a single site to characterize age trends and sex differences in actigraphy and self-report sleep dimensions across the healthy human lifespan. To accomplish this goal, we developed the Pittsburgh Lifespan Sleep Databank (PLSD), a large aggregate databank of participants from sleep research studies conducted at the University of Pittsburgh. Methods In the present analysis, we included N=1,070 PLSD participants from 21 studies without a major psychiatric, sleep, or medical condition. We used Generalized Additive Models to examine flexible, potentially non-linear relationships between age and sleep dimensions (actigraphy and self-report duration, efficiency, and timing; actigraphy variability) from ages 10 to 87. We also examined whether these sleep characteristics differed by sex across the lifespan. Results The most dramatic age-related trends were observed in sleep timing. Actigraphy and self-report sleep onset time shifted later between ages 10–18 and then shifted earlier again during the 20s. Actigraphy and self-report wake-up time also shifted earlier during the mid-20s through late 30s. Self-report duration became shorter from approximately ages 10–20. Self-report sleep efficiency and actigraphy variability both decreased over the entire lifespan. Relative to males, females tended to have earlier self-report sleep onset, higher actigraphy sleep efficiency, and longer actigraphy duration. Conclusion By focusing on lifespan sleep rather than specific age segments of the samples, we can provide a unified assessment of age-related changes and sex differences from childhood through older adulthood. An understanding of age trends and sex differences in sleep in healthy individuals – and explicating the timing and nature of these difference – can be used to identify periods of sleep-related risk or resilience and guide intervention efforts. Support (if any) University of Pittsburgh Clinical and Translational Science Institute (UL1TR001857).

Analysis of fine rooting below skid trails using linear and generalized additive models

2009 ◽  
Vol 39 (11) ◽  
pp. 2047-2058 ◽  
Author(s):  
Jürgen Schäffer ◽  
Klaus von Wilpert ◽  
Edgar Kublin
Keyword(s):  
Linear Models ◽  
Fine Root ◽  
Formal Model ◽  
Generalized Additive Models ◽  
Response Surfaces ◽  
Additive Models ◽  
Compacted Soils ◽  
Continuous Response ◽  
Model Following ◽  
Depth Gradients

Soil compaction caused by forest machinery changes the basic conditions for root propagation below skid trails. In consequence, lower fine-root densities have to be expected under wheel tracks compared with other skid trail strata that experience no direct traffic. Explorative data analysis of fine-root densities below a skid trail revealed that the fundamental assumptions for linear modelling were violated. Using a generalized linear model following a Poisson distribution with a log link function for the predictor variables together with an exponential covariance function to cope with spatial autocorrelation, the formal model criteria were met. In contrast to the linear models, generalized additive models provide flexible surface estimators that enable us to model continuous response surfaces. In addition, generalized additive models allow for the calculation of confidence intervals for the estimated density surface and for the use of inferential statistics, such as comparisons between depth gradients of fine rooting at distinct transect locations or depth layers. These model characteristics improve the possibility to recognize differences and to evaluate fine-root disturbances below skid trails without integrating uncertain strata information. They also enhance the options for determining the duration of time that is necessary to restore the rooting capacity on formerly compacted soils.

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