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Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0-5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.
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http://dx.doi.org/10.1038/s41467-017-01450-2 | DOI Listing |
Front Vet Sci
August 2025
Faculty of Fisheries, Recep Tayyip Erdogan University, Rize, Türkiye.
Application of anesthetic chemicals in aquaculture is important to minimize stress under normal operations such as handling, transport, and artificial breeding. In the past decade, the preference for natural anesthetics over synthetic ones has increased due to welfare issues regarding fish welfare and food safety. This study investigates the anesthetic efficacy of nutmeg oil () in three freshwater fish species- (Common carp), (Danube sturgeon), and (Rainbow trout)-by modeling behavioral (Induction and recovery times) and hematological responses using artificial neural networks (ANNs).
View Article and Find Full Text PDFVet World
July 2025
Department of Animal Production, Faculty of Animal Science, Hasanuddin University, Makassar 90245, South Sulawesi, Indonesia.
Background And Aim: The () gene is recognized as a critical regulator of ovarian function and fertility in cattle. However, its role in crossbred populations, particularly Madrasin cattle (Madura × Simmental cross), remains underexplored. Understanding the genetic underpinnings of fertility traits in this crossbreed could provide valuable insights for improving reproductive efficiency in Indonesia's livestock sector.
View Article and Find Full Text PDFAlpha Psychiatry
August 2025
Information Sciences and Technology, George Mason University, Fairfax, VA 22030, USA.
Background: Herein, we report on the initial development, progress, and future plans for an autonomous artificial intelligence (AI) system designed to manage major depressive disorder (MDD). The system is a web-based, patient-facing conversational AI that collects medical history, provides presumed diagnosis, recommends treatment, and coordinates care for patients with MDD.
Methods: The system includes seven components, five of which are complete and two are in development.
SAR QSAR Environ Res
August 2025
Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China.
Phosphorylation plays an important role in the activity of CDK2 and inhibitor binding, but the corresponding molecular mechanism is still insufficiently known. To address this gap, the current study innovatively integrates molecular dynamics (MD) simulations, deep learning (DL) techniques, and free energy landscape (FEL) analysis to systematically explore the action mechanisms of two inhibitors (SCH and CYC) when CDK2 is in a phosphorylated state and bound state of CyclinE. With the help of MD trajectory-based DL, key functional domains such as the loops L3 loop and L7 are successfully identified.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Key Laboratory of Intelligent Medical Imaging of Wenzhou, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored.
Purpose: To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer.