Road geometry estimation using vehicle trails: a linear mixed model approach

Author(s):  
Yi-Chen Zhang
Toxins ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 214
Author(s):  
Agathe Roucou ◽  
Christophe Bergez ◽  
Benoît Méléard ◽  
Béatrice Orlando

The levels of fumonisins (FUMO)—mycotoxins produced by Fusarium verticillioides—in maize for food and feed are subject to European Union regulations. Compliance with the regulations requires the targeting of, among others, the agroclimatic factors influencing fungal contamination and FUMO production. Arvalis-Institut du végétal has created a national, multiyear database for maize, based on field survey data collected since 2003. This database contains information about agricultural practices, climatic conditions and FUMO concentrations at harvest for 738 maize fields distributed throughout French maize-growing regions. A linear mixed model approach highlights the presence of borers and the use of a late variety, high temperatures in July and October, and a water deficit during the maize cycle as creating conditions favoring maize contamination with Fusarium verticillioides. It is thus possible to target a combination of risk factors, consisting of this climatic sequence associated with agricultural practices of interest. The effects of the various possible agroclimatic combinations can be compared, grouped and classified as promoting very low to high FUMO concentrations, possibly exceeding the regulatory threshold. These findings should facilitate the creation of a national, informative and easy-to-use prevention tool for producers and agricultural cooperatives to manage the sanitary quality of their harvest.


2016 ◽  
Vol 64 (2) ◽  
pp. 163-167
Author(s):  
Tahmidul Islam ◽  
Md Golam Rabbani ◽  
Wasimul Bari

Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model performances have also been compared. Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)


2017 ◽  
Vol 22 (6) ◽  
pp. 784-793 ◽  
Author(s):  
Mona Diegelmann ◽  
Carl-Philipp Jansen ◽  
Hans-Werner Wahl ◽  
Oliver K. Schilling ◽  
Eva-Luisa Schnabel ◽  
...  

2019 ◽  
Vol 220 ◽  
pp. 37-45 ◽  
Author(s):  
Tianpeng Chang ◽  
Julong Wei ◽  
Xiaoqiao Wang ◽  
Jian Miao ◽  
Lingyang Xu ◽  
...  

2018 ◽  
Vol 51 (1) ◽  
pp. 180-186 ◽  
Author(s):  
Rachel Moore ◽  
◽  
Francesco Paolo Casale ◽  
Marc Jan Bonder ◽  
Danilo Horta ◽  
...  

2018 ◽  
Author(s):  
Rachel Moore ◽  
Francesco Paolo Casale ◽  
Marc Jan Bonder ◽  
Danilo Horta ◽  
Lude Franke ◽  
...  

AbstractDifferent environmental factors, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (GxE). Although high-dimensional environmental data are increasingly available, and multiple environments have been implicated with GxE at the same loci, multi-environment tests for GxE are not established. Such joint analyses can increase power to detect GxE and improve the interpretation of these effects. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to test for and characterize loci that interact with multiple environments. After validating our model using simulations, we apply StructLMM to body mass index in UK Biobank, where our method detects previously known and novel GxE signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.


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