Medicago truncatula is used as a model plant for exploring the genetic and molecular determinants of nitrogen (N) nutrition in legumes. In this study, our aim was to detect quantitative trait loci (QTL) controlling plant N nutrition using a simple framework of carbon/N plant functioning stemming from crop physiology. This framework was based on efficiency variables which delineated the plant's efficiency to take up and process carbon and N resources. A recombinant inbred line population (LR4) was grown in a glasshouse experiment under two contrasting nitrate concentrations. At low nitrate, symbiotic N(2) fixation was the main N source for plant growth and a QTL with a large effect located on linkage group (LG) 8 affected all the traits. Significantly, efficiency variables were necessary both to precisely localize a second QTL on LG5 and to detect a third QTL involved in epistatic interactions on LG2. At high nitrate, nitrate assimilation was the main N source and a larger number of QTL with weaker effects were identified compared to low nitrate. Only two QTL were common to both nitrate treatments: a QTL of belowground biomass located at the bottom of LG3 and another one on LG6 related to three different variables (leaf area, specific N uptake and aboveground:belowground biomass ratio). Possible functions of several candidate genes underlying QTL of efficiency variables could be proposed. Altogether, our results provided new insights into the genetic control of N nutrition in M. truncatula. For instance, a novel result for M. truncatula was identification of two epistatic interactions in controlling plant N(2) fixation. As such this study showed the value of a simple conceptual framework based on efficiency variables for studying genetic determinants of complex traits and particularly epistatic interactions.
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http://dx.doi.org/10.1007/s00122-011-1744-z | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
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J Craniofac Surg
January 2025
Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, School of Medicine, Shanghai Jiao Tong University.
Background: This paper presents the authors' team's research on a craniofacial surgical robot developed in China. Initiated in 2011 with government funding, the craniofacial surgical robot project was officially launched in Shanghai, developed jointly by the Ninth People's Hospital affiliated with Shanghai Jiao Tong University School of Medicine and the Shanghai Jiao Tong University medical-engineering team. Currently, based on multiple rounds of model surgeries, animal experiments, and clinical trials, our team is applying for approval as a Class III medical device from the National Medical Products Administration (NMPA).
View Article and Find Full Text PDFPLoS One
January 2025
Medical Image Processing Research Group (MIPRG), Dept. of Elect. & Comp. Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and respiratory motion. Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been proposed to accelerate MRI data acquisition and improve image quality. However, these methods have limitations in high spatial-resolution applications, often resulting in blurring or residual artifacts.
View Article and Find Full Text PDFMikrochim Acta
January 2025
School of Public Health, Hebei Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, No. 21 Bohai Road, Caofeidian, Tangshan, 063210, Hebei, China.
Biochars (BCs) derived from waste-branches of apple tree, grape tree, and oak were developed for direct solid-phase extraction (SPE) of five benzodiazepines (BZDs) in crude urine samples prior to liquid chromatography-tandem mass spectrometry (LC-MS/MS) determination. Scanning electron microscopy, elemental analyzer, X-ray diffractometry, N adsorption/desorption experiments, and Fourier transform infrared spectrometry characterizations revealed the existence of their mesoporous structure and numerous oxygen-containing functional groups. The obtained BCs not only possessed high affinity towards BZDs via π-π and hydrogen bond interactions, but also afforded the great biocompatibility of excluding interfering components from undiluted urine samples when using SPE adsorbents.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
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