Publications by authors named "G GERARD"
Proliferative Vitreoretinopathy (PVR) is a major complication of surgical repair for Rhegmatogenous Retinal Detachment (RRD). Methotrexate (MTX), a folate antimetabolite, has shown promise in targeting the pathological processes involved in PVR, such as cell proliferation inhibition, fibrosis and anti-inflammation. Systematic review examines the use of MTX in PVR by analysing different administration methods and outcomes.
View Article and Find Full Text PDFIn plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations.
View Article and Find Full Text PDFArticle Synopsis
- Statistical machine learning (ML) analyzes large volumes of genomic, phenotypic, and environmental data to uncover patterns and improve prediction models in plant breeding.
- By investigating genotype-by-environment (G×E) interactions, ML helps identify genetic factors that influence performance in various environments.
- This review emphasizes how big data and ML enhance prediction accuracy and streamline breeding strategies through comprehensive analysis of diverse datasets.
Article Synopsis
- The study looked at how atopic dermatitis (AD), a skin condition, can affect children's growth in height.
- Researchers checked a lot of articles to see if kids with AD were shorter than those without, and found mixed results: some studies linked AD to being shorter, while others did not.
- They found that kids with more severe AD, those who had it from a young age, had trouble sleeping, or had certain diet restrictions were more likely to have growth problems.
This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations.
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