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Influence of Source of Origin and Region of Finishing on Growth Performance and Carcass Characteristics of Finishing Heifers Fed in the United States.
J Anim Sci
January 2025
Department of Animal Science, South Dakota State University, Brookings, SD, USA.
The objective was to evaluate growth performance and carcass traits of finishing beef heifers sourced and finished in different regions in the U.S. Heifers [n = 190; initial body weight (BW) 483 ± 0.
View Article and Find Full Text PDFN-acetyl-tryptophan in Acute Kidney Injury after Cardiac Surgery.
J Am Soc Nephrol
January 2025
State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China.
Background: Cardiac surgery-associated acute kidney injury is a common serious complication after cardiac surgery. Currently, there are no specific pharmacological therapies. Our understanding of its pathophysiology remains preliminary.
View Article and Find Full Text PDFDating the bacterial tree of life based on ancient symbiosis.
Syst Biol
January 2025
Simon F. S. Li Marine Science Laboratory, School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR.
Obtaining a timescale for bacterial evolution is crucial to understand early life evolution but is difficult owing to the scarcity of bacterial fossils. Here, we introduce multiple new time constraints to calibrate bacterial evolution based on ancient symbiosis. This idea is implemented using a bacterial tree constructed with genes found in the mitochondrial lineages phylogenetically embedded within Proteobacteria.
View Article and Find Full Text PDFPredicting transcriptional changes induced by molecules with MiTCP.
Brief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
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