Zheyuan Wang

Institute of Crop Sciences

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Jinhua Fu

Chinese Academy of Civil Aviation Science and Technology

Wenjuan Tang

Shanghai Children's Hospital

Likai Zhuang

Children's Hospital of Fudan University

Yunjun Liu

Institute of Crop Sciences

Xiang Ren

Shanghai Children's Hospital

Min Wu

Institute for Infocomm Research

Mingfang Han

Hangzhou Normal University

Zhanchi Li

Shanghai Jiao Tong University

Junjie Fu

Chinese Academy of Agricultural Sciences

Yuhe Duan

Affiliated Hospital of Qingdao University

Cooperative Institutions

Institute of Crop Sciences

Chinese Academy of Agricultural Sciences

Shanghai Jiao Tong University

Shanghai Children's Hospital

China Agricultural University

First Affiliated Hospital of Fujian Medical University

Fujian Medical University

Children's Hospital of Fudan University

Guizhou Academy of Agricultural Sciences

Chinese Academy of Civil Aviation Science and Technology

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Genome‐wide expression quantitative trait locus analysis reveals silk‐preferential gene regulatory network in maize

Physiologia Plantarum (2024)

Xiaoli Wang Jiawen Lu Mingfang Han Zheyuan Wang Hongwei Zhang

Abstract Silk of maize ( Zea mays L. ) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome‐wide identification of silk‐preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk‐preferential genes that were specifically or preferentially expressed in silk using our population. Silk‐preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans ‐eQTL regulation played a more important role for silk‐preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk‐preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk‐preferential gene Bx10 , which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk‐preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.

Gene regulatory network

10.1111/ppl.14386

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Development and multi-institutional validation of a deep learning model for grading of vesicoureteral reflux on voiding cystourethrogram: a retrospective multicenter study

EClinicalMedicine (2024)

Zhanchi Li Zelong Tan Zheyuan Wang Wenjuan Tang Xiang Ren

Voiding cystourethrography (VCUG) is the gold standard for the diagnosis and grading of vesicoureteral reflux (VUR). However, VUR grading from voiding cystourethrograms is highly subjective with low reliability. This study aimed to develop a deep learning model to improve reliability for VUR grading on VCUG and compare its performance to that of clinicians.

Voiding cystourethrogram

Grading (engineering)

Gold standard (test)

10.1016/j.eclinm.2024.102466

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