Xinyu Guo

China Agricultural University

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i-10 index

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Weiliang Wen

National Engineering Research Center for Information Technology in Agriculture

Xianju Lu

National Engineering Research Center for Information Technology in Agriculture

Chunjiang Zhao

National Engineering Research Center for Information Technology in Agriculture

Shenglian Lu

Guangxi Normal University

Jianjun Du

National Engineering Research Center for Information Technology in Agriculture

Chunjiang Zhao

National Engineering Research Center for Information Technology in Agriculture

Shenghao Gu

Beijing Academy of Agricultural and Forestry Sciences

Chuanyu Wang

Anhui Medical University

Sheng Wu

National Engineering Research Center for Information Technology in Agriculture

Boxiang Xiao

Beijing Institute of Fashion Technology

Cooperative Institutions

National Engineering Research Center for Information Technology in Agriculture

100

China Agricultural University

Chinese Academy of Sciences

Zhejiang University

Ministry of Agriculture and Rural Affairs

Beijing Academy of Agricultural and Forestry Sciences

Sun Yat-sen University

Stomatology Hospital

Wenzhou Medical University

Peking University

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Virtual realization of tobacco leaves based on ball B-spline function

Nongye gongcheng xuebao (2011)

Yunyun Wang Weiliang Wen Xinyu Guo Zhao Guohui Shenglian Lu

Spline (mechanical)

Realization (probability)

Ball (mathematics)

Tobacco leaf

10.3969/j.issn.1002-6819.2011.1.037

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Citations (3)

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Yinglun Li Weiliang Wen Xinyu Guo Zetao Yu Shenghao Gu

Abstract Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or ground high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R 2 =0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

10.1101/2020.10.19.345199

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Citations (2)

Low calcium-induced delay in development of root apoplastic barriers enhances Cd uptake and accumulation in Sedum alfredii

The Science of The Total Environment (2020)

Yuankun Liu Qi Tao Xinyu Guo Jipeng Luo Jinxing Li

Suberin

Endodermis

Symplast

10.1016/j.scitotenv.2020.137810

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Citations (36)

The community structure identification for the Chinese merger and acquisition network

Physica A Statistical Mechanics and its Applications (2019)

Xinyu Guo Kai Yang Xianming Wu Qiang Guo

Modularity

Clustering coefficient

10.1016/j.physa.2019.04.133

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Citations (0)

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning

Ecotoxicology and Environmental Safety (2024)

Yi Lu Linjie Nie Xinyu Guo Tiantian Pan Rongqin Chen

Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.

10.1016/j.ecoenv.2024.116704

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Citations (4)

Analyses of factors influencing Chinese repeated blood donation behavior

Industrial Management & Data Systems (2019)

Xu Chen Shuyao Wu Xinyu Guo

Purpose The purpose of this paper is to link subjective data obtained from a questionnaire survey with blood donation behavioral data, constructs a conceptual model of the factors that influence repeated blood donation behavior, and explores the mechanisms and degrees of influence of the value and cost elements of blood donors on repeated blood donation behavior. Design/methodology/approach First, this study constructs a conceptual model of the factors that affect repeated blood donation based on delivered value theory. Second, this paper is driven by subjective data obtained from a questionnaire and big data on blood donation behavior; the use of multisource data can help us understand repeated blood donation behavior from a broader perspective. Through data association and systematic research, it is possible to accurately explore the mechanisms through which various factors affect repeated blood donation behavior. Findings The results show that among the value elements, personnel value (PV), image value and blood donation value affect blood donation behavior in decreasing order. The change in PV per unit directly caused a 0.471-unit change in satisfaction, which indirectly caused a 0.098-unit change in donation behavior. Among the cost elements of blood donors, only the impact of time cost (TC) on repeated blood donation behavior was significant, and a change of one unit in TC caused a change in repeated blood donation behavior of −0.035 units. In addition, this paper groups subjects according to gender, education and age and explores the differences in the value and cost factors of different groups. Finally, based on the research results, the authors propose corresponding policy recommendations. Originality/value First, the authors expand the application field of the delivered value theory, and provide a new perspective for studying repeated blood donation. Second, through questionnaire data and blood donation behavior data, the authors comprehensively explore the factors that influence repeated blood donation behavior.

Affect

Repeated measures design

Value (mathematics)

10.1108/imds-09-2019-0509

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Citations (15)

Venation Skeleton-based Modeling of Rice Leaf

Qing Keleqiqige Xinyu Guo Boxiang Xiao Weiliang Wen

A 3D geometrical model of plant leaf if constructed based on the morphological structure of plant leaf. The modeling method is illustrated by rice leaf as an example, which has a structure of leaf venations. We present a precise modeling method to model the plant leaf with parallel leaf venations. In the model, the leaf has apparent leaf venations and mesophyll. B-spline curve was used to generate leaf venation skeleton in rice blade and rice sheath, and then disk B-spline was used to generate leaf venation in rice blade and sheath. Furthermore, mesophyll was generated in rice blade and sheath based on leaf venation skeleton. The experimental results show that the visualization model has highly realistic effect and can also be expanded to modeling of other plants' leaves.

Leaf blade

Rice plant

Skeleton (computer programming)

10.1109/icicta.2011.362

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Case Report: Identification of Germline Chimerism in Monochorionic Dizygotic Twins

Frontiers in Genetics (2021)

Juan Chen Jian Xu Zhiheng Chen Min-Na Yin Xinyu Guo

Monochorionic twins are generally considered to be monozygotic, as monochorionic dizygotic (MCDZ) twins are extremely rare in natural pregnancies. Several studies have reported this rare occurrence, and most of these pregnancies have been conceived by assisted reproductive technology (ART). These reports mostly focused on MCDZ twin pregnancies and the childhood development of the twins; a follow-up into adulthood and the effect on their reproduction has not been reported. In this case study, we report a case of chimerism in opposite-sex MCDZ twins who were naturally conceived and have reached reproductive maturity. We collected oral mucosal, endometrial, and germ cells from the twins and evaluated their chimerism using single-nucleotide polymorphism (SNP) array and droplet digital PCR (ddPCR). The SNP array showed that they had 4,049 non-allele shared loci, and they inherited nearly 50% informative SNP loci from each parent, confirming that they are dizygotic twins. We found that the female twin had a 46, XX (2)/46, XY (78) karyotype in her peripheral blood. The SNP array confirmed that the female twin and male twin had the same blood haplotype. The ddPCR result showed 92.84 (± 1.80%) chimerism in her blood. In case of chimerism in her germline, the female twin chose preimplantation genetic testing for aneuploidy for her blastocysts. Fortunately, the patient only had blood chimerism. A healthy boy was born at 39 weeks of gestation.

Zygosity

SNP

SNP array

Twin Pregnancy

Monozygotic twin

10.3389/fgene.2021.744890

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Citations (5)

Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high‐throughput phenotypic analysis

Plant Biotechnology Journal (2020)

Ying Zhang Jinglu Wang Jianjun Du Yanxin Zhao Xianju Lu

Summary High‐throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time‐consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high‐throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro‐CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry‐related, morphology‐related and distribution‐related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro‐phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome‐wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro‐phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.

Genetic architecture

Phenotypic trait

Genome-wide Association Study

Candidate gene

Trait

Association mapping

Inbred strain

10.1111/pbi.13437

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Citations (37)

Maize plant drought stress phenotype testing method based on time-series images

Nongye gongcheng xuebao (2016)