Evidence of a range expansion in sunfish from 47 years of coastal sightings

Almost nothing is known about the historical abundance of the ocean sunfish. Yet as an ecologically and functionally important taxa, understanding changes in abundance may be a useful indicator of how our seas are responding to anthropogenic changes including overfishing and climate change. Within this context, sightings from a coastal bird observatory (51.26$$^\circ$$ ∘ N, 9.30$$^\circ$$ ∘ W) over a 47 year period (from April to October 1971–2017) provided the first long-term index of sunfish abundance. Using a general linear mixed effect model with a hurdle to deal with imperfect detectability and to model trends, a higher probability of detecting sunfish was found in the 1990s and 2000s. Continuous Plankton Recorder (CPR) phytoplankton color indices and the annual mean position of the 13 $$^{\circ }$$ ∘ C sea surface isotherm were significantly correlated with the probability of detecting sunfish. An increase in siphonophore abundance (as measured by the CPR) was also documented. However, this increase occurred 10–15 years after the sunfish increase and was not significantly correlated with sunfish abundance. Our results suggest that the observed increase in sunfish sightings is evidence of a range expansion because it was significantly correlated with the mean position of the 13 $$^{\circ }$$ ∘ C isotherm which moved northwards by over 200 km. Furthermore, the observed increase in sunfish occured 10 years before sunfish sightings are documented in Icelandic and Norwegian waters, and was concurrent with well-known range expansions for other fish species during the 1990s. This study demonstrates how sustained citizen science projects can provide unique insights on the historical abundance of this enigmatic species.

On directors’ compensation: a multilevel analysis of Spanish listed companies

This study analyzes the determinants of the annual compensation of directors belonging to the boards of the Spanish companies that constitute the IBEX 35 stock index. We investigate the importance of observed and unobserved heterogeneity in explaining director compensation. Based on a three-level mixed effect model, our analysis includes time-invariant random effects at company and manager level as determinants of director pay. We find that company effects explain 30% of the variation in director pay, while company and director effects taken together explain 77% of that variation. Our findings suggest that the characteristics of the company, in terms of activity sector, size and financial performance, and the professional attributes of the director (especially the role within the board), influence the compensation received. In addition, some directors and companies show random effects (either positive or negative) that significantly separate them from the expected compensation estimated from the fixed part of the model.

Covariance Generalized Linear Models: An Approach for Quantifying Uncertainty in Tree Stem Taper Modeling

A natural dependence among diameters measured within-tree is expected in taper data due to the hierarchical structure. The aim of this paper was to introduce the covariance generalized linear model (CGLM) framework in the context of forest biometrics for Pinus taeda stem form modeling. The CGLMs are based on marginal specification, which requires a definition of the mean and covariance components. The tree stem mean profiles were modeled by a nonlinear segmented model. The covariance matrix was built considering four strategies of linear combinations of known matrices, which expressed the variance or correlations among observations. The first strategy modeled only the variance of the diameters over the stem as a function of covariates, the second modeled correlation among observations, the third was defined based on a random walk model, the fourth was based on a structure similar to a mixed-effect model with a marginal specification, and the fourth was a traditional mixed-effect model. Mean squared error and bias showed that the approaches were similar for describing the mean profile for fitting and validation dataset. However, uncertainties expressed by confidence intervals of the relative diameters were significant and related to the matrix covariance structures of the CGLMs. Study Implications: We proposed stem taper modeling based on a new class of statistical models. Covariance generalized linear models allow quantification of the stem dynamic by using a nonlinear model. Uncertainty estimates are performed on a covariance matrix given by a linear combination of known matrices. The matrices enable modeling of the nonconstant variance as well as the several correlation patterns, resulting in a framework more flexible and robust than traditional approaches usually applied for stem taper modeling. For practical purposes, uncertainty modeling can improve forest management planning, because the production limits by timber assortments are more reliable due to the confidence intervals derived from an appropriate uncertainty analysis.

Hiệu quả can thiệp thay đổi lối sống cho học sinh lớp 6 thành phố Hồ Chí Minh

Can thiệp dựa vào bạn đồng trang lứa là hướng đi mới nhiều hứa hẹn nhằm phòng ngừa béo phì cho trẻ vị thành niên. Nghiên cứu này nhằm so sánh sự thay đổi về chế độ ăn uống và thời gian hoạt động thể lực, tĩnh tại giữa 2 nhóm học sinh nhóm can thiệp và nhóm chứng sau 6 tháng can thiệp. Đối tượng là học sinh lớp 6 các trường cấp 2, thành phố Hồ Chí Minh (4 trường chứng - 4 trường can thiệp), 84 học sinh/trường. Thời gian nghiên cứu từ tháng 9/2018 đến tháng 4/2020. Phân tích Mixed Effect Model được dùng để so sánh sự thay đổi về chế độ ăn uống và thời gian hoạt động thể lực, tĩnh tại trước - sau can thiệp ở mỗi nhóm và so sánh sự thay đổi giữa 2 nhóm chứng và can thiệp, có hiệu chỉnh với giới tính, tuổi, chỉ số khối cơ thể (BMI) lúc ban đầu, và cụm trường. Chương trình can thiệp như sau: các trưởng nhóm học sinh lớp 8 tập huấn 4 bài về dinh dưỡng và vận động cho học sinh lớp 6 của toàn trường can thiệp. Hệ thống hỗ trợ được triển khai để đảm bảo sự tuân thủ của học sinh trong 6 tháng sau can thiệp. Nhóm trường chứng vẫn theo chương trình thường qui của Bộ Giáo Dục. Sau can thiệp, nhóm can thiệp tăng hoạt động thể lực 8 ± 3,5 phút/ngày (p < 0,05), giảm hoạt động tĩnh tại 35 ± 9,9 phút/ngày (p < 0,05); giảm lượng ngũ cốc tiêu thụ 62 ± 19,0 gam/ngày (p < 0,05), giảm uống nước ngọt 0,79 lần; tăng ăn trái cây 36 ± 15,0 gam/ngày (p < 0,05) so với nhóm chứng, đã hiệu chỉnh với giới tính, tuổi, BMI ban đầu. Như vậy, nghiên cứu đã chứng minh được hiệu quả của can thiệp dựa trên trường học thông qua nhóm bạn đồng trang lứa thay đổi lối sống tích cực cho học sinh lớp 6 thành phố Hồ Chí Minh .

Nintedanib in idiopathic and secondary pleuroparenchymal fibroelastosis

Background Pleuroparenchymal fibroelastosis (PPFE) has a variable disease course with dismal prognosis in the majority of patients with no validated drug therapy. This study is to evaluate the effect of nintedanib in patients with idiopathic and secondary PPFE. Patients admitted to a tertiary care center (2010–2019) were included into this retrospective analysis if they had a multidisciplinary diagnosis of PPFE, had been followed-up for 3 months or more, and had lung function tests and chest CTs available for review. Changes in pulmonary function tests were assessed using non-parametric tests and linear mixed effect model. Lung volumes were measured with lobar segmentation using chest CT. Results Out of 21 patients with PPFE, nine had received nintedanib, six had received another treatment and another six patients were monitored without drug therapy. Annual FVC (% of predicted) relative decline was − 13.6 ± 13.4%/year before nintedanib and − 1.6 ± 6.02%/year during nintedanib treatment (p = 0.014), whereas no significant change in FVC% relative decline was found in patients receiving another treatment (− 13.25 ± 34 before vs − 16.61 ± 36.2%/year during treatment; p = 0.343). Using linear mixed effect model, the slope in FVC was − 0.97%/month (95% CI: − 1.42; − 0.52) before treatment and − 0.50%/month (95% CI: − 0.88; 0.13) on nintedanib, with a difference between groups of + 0.47%/month (95% CI: 0.16; 0.78), p = 0.004. The decline in the upper lung volumes measured by CT was − 233 mL/year ± 387 mL/year before nintedanib and − 149 mL/year ± 173 mL/year on nintedanib (p = 0.327). Nintedanib tolerability was unremarkable. Conclusion In patients with PPFE, nintedanib treatment might be associated with slower decline in lung function, paving the way for prospective, controlled studies.

Hofstede’s cultural dimensions in the gravity model using mixed-effect model

According to the gravity model, the closer the distance between two countries, the trade activity between those countries will be greater. This research aims to know whether the gravity model works on Indonesian export value in 2002-2019. Besides the distance, this research also study the effects of population, per capita PPP GDP and Hofstede cultural dimensions. The research was done using a mixed-effect model. The results of this study show that together, population, per capita PPP GDP, distance and Hofstede cultural dimensions have significant effects on Indonesian export value. The gravity model is proven in this study because distance has a negative effect on export value. Individually, population, per capita PPP GDP and long-term orientation index have a positive significant effect while masculinity index has a negative significant effect on export value. Indonesian export values tend to be elastic toward per capita PPP GDP, population and the distance. This research also found that the effect of per capita PPP GDP has a random effect or its effect is difference among Indonesia’s main trade partners.

A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals’ daily nervousness. The article is accompanied by an R package (called ) and functions that support users in the application of the suggested models.

Bayesian multi-response nonlinear mixed-effect model: application of two recent HIV infection biomarkers

Since the discovery of the human immunodeficiency virus (HIV) 35 years ago, the epidemic is still ongoing in France. To monitor the dynamics of HIV transmission and assess the impact of prevention campaigns, the main indicator is the incidence. One method to estimate the HIV incidence is based on biomarker values at diagnosis and their dynamics over time. Estimating the HIV incidence from biomarkers first requires modeling their dynamics since infection using external longitudinal data. The objective of the work presented here is to estimate the joint dynamics of two biomarkers from the PRIMO cohort. We thus jointly modeled the dynamics of two biomarkers (TM and V3) using a multi-response nonlinear mixed-effect model. The parameters were estimated using Bayesian Hamiltonian Monte Carlo inference. This procedure was first applied to the real data of the PRIMO cohort. In a simulation study, we then evaluated the performance of the Bayesian procedure for estimating the parameters of multi-response nonlinear mixed-effect models.