Publications by authors named "M G Sturrock"

Article Synopsis
  • Genomic selection (GS) in aquatic animal breeding is gaining traction for its improved accuracy and speed over traditional pedigree methods, with artificial intelligence (AI) playing a key role.
  • Deep learning (DL) techniques, such as deep neural networks (DNNs) and convolutional neural networks (CNNs), are being utilized to enhance phenotyping, genotyping, and genomic estimated breeding value (GEBV) predictions.
  • The article suggests that as DL technology advances, it will likely be applied to a wider range of aquaculture species in molecular breeding efforts.
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Unlabelled: The papilla number is one of the most economically important traits of sea cucumber in the China marketing trade. However, the genetic basis for papilla number diversity in holothurians is still scarce. In the present study, we conducted genome-wide association studies (GWAS) for the trait papilla number of sea cucumbers utilizing a set of 400,186 high-quality SNPs derived from 200 sea cucumbers.

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Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets.

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Genotypic and phenotypic adaptation is the consequence of ongoing natural selection in populations and is key to predicting and preventing drug resistance. Whereas classic antibiotic persistence is all-or-nothing, here we demonstrate that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in growing populations. Furthermore, we report the potentially wide-spread nature of this form of emergent gene expression (EGE) by instantaneous phenotypic selection process under bactericidal and bacteriostatic antibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions.

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Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results.

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