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Synergistic effects of GmLFYa and GmLFYb on Compound Leaf Development in Soybean.

Physiol Plant

January 2025

School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.

Legume leaves exhibit diverse compound forms, with various regulatory mechanisms underlying the development. The transcription factor-encoding KNOXI genes are required to promote leaflet initiation in most compound-leafed angiosperms. In non-IRLC (inverted repeat-lacking clade) legumes, KNOXI are expressed in compound leaf primordia but not in others (IRLC).

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Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.

Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.

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Oocyte/zygote/embryo maturation arrest: a clinical study expanding the phenotype of NOBOX variants.

J Assist Reprod Genet

January 2025

Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Clinical Sciences, Research Group Genetics, Reproduction and Development, Centre for Medical Genetics, Laarbeeklaan 101, 1090, Brussels, Belgium.

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Background: Urine neutrophil gelatinase-associated lipocalin (uNGAL) is a biomarker for the early diagnosis of AKI.

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In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.

J Imaging Inform Med

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Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.

The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.

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