|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | High-Resolution Genome-wide Association Study Identifies Genomic Regions and Candidate Genes for Important Agronomic Traits in Wheat显示文摘Wheat(Triticum aestivum)is a major staple food crop worldwide.Genetic dissection of important agronomic traits is essential for continuous improvement of wheat yield to meet the demand of the world's growing population.We conducted a large-scale genome-wide association study(GWAS)using a panel of 768 wheat cultivars that were genotyped with 327609 single-nucleotide polymorphisms generated by genotyping-by-sequencing and detected 395 quantitative trait loci(QTLs)for 12 traits under 7 environments.Among them,273 QTLs were delimited to≤1.0-Mb intervals and 7 of them are either known genes(Rht-D,Vrn-B1,and Vrn-D1)that have been cloned or known QTLs(TaGA2ox8,APO1,TaSus1-7B,and Rht12)that were previously mapped.Eight putative candidate genes were identified for three QTLs that enhance spike seed setting and grain size using gene expression data and were validated in three bi-parental populations.Protein sequence analysis identified 33 putative wheat orthologs that have high identity with rice genes in QTLs affecting similar traits.Large r^2 values for additive effects observed among the QTLs for most traits indicated that the phenotypes of these identified QTLs were highly predictable.Results from this study demonstrated that significantly increasing GWAS population size and marker density greatly improves detection and identification of candidate genes underlying a QTL,solidifying the foundation for large-scale QTL fine mapping,candidate gene validation,and developing functional markers for genomics-based breeding in wheat. | Yunlong Pang Chunxia Liu Danfeng Wang Paul St.Amand Amy Bernardo Wenhui Li Fang He Linzhi Li Liming Wang Xiufang Yuan Lei Dong Yu Su Huirui Zhang Meng Zhao Yunlong Liangi Hongze Jia Xitong Shen Yue Lu Hongming Jiang Yuye Wu Anfei Li Honggang Wang Lingrang Kong Guihua Bai Shubing Liu | 2020 | Molecular Plant2020，13，9： | 8 |
| 2 | The advanced development of molecular targeted therapy for hepatocellular carcinoma显示文摘Hepatocellular carcinoma(HCC),one of the most common malignant tumors in China,severely threatens the life and health of patients.In recent years,precision medicine,clinical diagnoses,treatments,and innovative research have led to important breakthroughs in HCC care.The discovery of new biomarkers and the promotion of liquid biopsy technologies have greatly facilitated the early diagnosis and treatment of HCC.Progress in targeted therapy and immunotherapy has provided more choices for precise HCC treatment.Multiomics technologies,such as genomics,transcriptomics,and metabolomics,have enabled deeper understanding of the occurrence and development mechanisms,heterogeneity,and genetic mutation characteristics of HCC.The continued promotion and accurate typing of HCC,accurate guidance of treatment,and accurate prognostication have provided more treatment opportunities and prolonged survival timelines for patients with HCC.Innovative HCC research providing an in-depth understanding of the biological characteristics of HCC will be translated into accurate clinical practices for the diagnosis and treatment of HCC. | Tao Yan Lingxiang Yu Ning Zhang Caiyun Peng Guodong Su Yi Jing Linzhi Zhang Tong Wu Jiamin Cheng Qian Guo Xiaoliang Shi Yinying Lu | 2022 | Cancer Biology & Medicine2022，19，6： | 2 |
| 3 | Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm 显示文摘 | Su Linzhi Gong Maoguo Sun Bo | 2014 | International Journal of Remote Sensing2014，35，2： | 1 |
| 4 | Fuzzy clusteringwith a modified MRF energy function for change detection insynthetic aperture radar images 显示文摘 | Gong Maoguo Su Linzhi Jia Meng | 2014 | IEEE Trans on FuzzySystem2014，22，1： | 1 |
| 5 | Detecting changes ofthe Yellow River estuary via SAR images based on local fit-search model and kernel-induced graph cuts 显示文摘 | Gong Maoguo Jia Meng Su Linzhi | 2014 | International Journal of Remote Sensing2014，35，1112： | 1 |
| 6 | GCR-Net:3D Graph convolution-based residual network for robust reconstruction in cerenkov luminescence tomography显示文摘Cerenkov Luminescence Tomography(CLT)is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes.However,due to severe ill-posed inverse problem,obtaining accurate reconstruction results is still a challenge for traditional model-based methods.The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source,which effectively improves the performance of CLT reconstruction.However,the previously proposed deep learning-based methods cannot work well when the order of input is disarranged.In this paper,a novel 3D graph convolution-based residual network,GCR-Net,is proposed,which can obtain a robust and accurate reconstruction result from the photon intensity of the surface.Additionally,it is proved that the network is insensitive to the order of input.The performance of this method was evaluated with numerical simulations and in vivo experiments.The results demonstrated that compared with the existing methods,the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing threedimensional information. | Weitong Li Mengfei Du Yi Chen Haolin Wang Linzhi Su Huangjian Yi Fengjun Zhao Kang Li Lin Wang Xin Cao | 2023 | Journal of Innovative Optical Health Sciences2023，16，1： | 0 |
| 7 | A novel denoising framework for cerenkov luminescence imaging based on spatial information improved clustering and curvature-driven diffusion显示文摘With widely availed clinically used radionuclides,Cer enkov luminescence imaging(CLI)has become a potential tool in the field of optical molecular imaging.However,the impulse noises introduced by high-energy gamma rays that are generated during the decay of radionuclide reduce the image quality significantly,which affects the acauracy of quantitative analysis,as well as the three dimensional reconstruction.In this work,a novel denoising framework based on fuzzy dlustering and curvat ure driven difusion(CDD)is proposed to remove this kind of impulse noises.To improve the accuracy,the F u1zzy Local Information C-Means algorithm,where spatial information is evolved,is used.We evaluate the per formance of the proposed framework sys-tematically with a series of experiments,and the corresponding results demonstrate a better denoising effect than those from the commonly used median filter method.We hope this work may provide a useful data pre processing tool for CLI and its following studies. | Xin Cao Yi Sun Fei Kang Lin Wang Huangjian Yi Fengjun Zhao Linzhi Su Xiaowei He | 2018 | Journal of Innovative Optical Health Sciences2018，，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员