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RISS 인기검색어
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- 저자 : Yanjie Zhu (검색결과 4 건)
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( Yanjie Liu ),( Longfei Chen ),( Qiaohong Xie ),( Xi Yu ),( Anqing Duan ),( Yamin Lin ),( Biyun Xiang ),( Xiaoran Hao ),( Wanwan Chen ),( Xudong Zhu ) 한국미생물생명공학회(구 한국산업미생물학회) 2019 Journal of microbiology and biotechnology Vol.29 No.10
The fungal products dibenzodioxocinones promise a novel class of inhibitors against cholesterol ester transfer protein (CEPT). Knowledge as to their biosynthesis is scarce. In this report, we characterized four more dibenzodioxocinones, which along with a previously described member pestalotiollide B, delimit the dominant spectrum of secondary metabolites in P. microspora. Through mRNA-seq profiling in gα1Δ, a process that halts the production of the dibenzodioxocinones, a gene cluster harboring 21 genes including a polyketide synthase, designated as pks8, was defined. Disruption of genes in the cluster led to loss of the compounds, concluding the anticipated role in the biosynthesis of the chemicals. The biosynthetic route to dibenzodioxocinones was temporarily speculated. This study reveals the genetic basis underlying the biosynthesis of dibenzodioxocinone in fungi, and may facilitate the practice for yield improvement in the drug development arena.
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Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint
Ziwen Ke,Yanjie Zhu,Dong Liang 대한자기공명의과학회 2020 Investigative Magnetic Resonance Imaging Vol.24 No.4
Dynamic magnetic resonance (MR) imaging has generated great research interest, because it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is still a challenge for dynamic MR imaging. Most existing methods reconstruct dynamic MR images from incomplete k -space data under the guidance of compressed sensing (CS) or lowrank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still some smoothing needed in the reconstructed images at high acceleration. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhanced loss constraint, dubbed cascaded residual dense networks (CRDN). Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion. We further use the higher-degree total variation loss function, which has the edge enhancement properties, for training the networks.
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DEMO: Deep MR Parametric Mapping with Unsupervised Multi-Tasking Framework
Jing Cheng,Yuanyuan Liu,Yanjie Zhu,Dong Liang 대한자기공명의과학회 2021 Investigative Magnetic Resonance Imaging Vol.25 No.4
Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learningbased methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learningbased framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.
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A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction
Zhuonan He,Cong Quan,Siyuan Wang,Yuanzheng Zhu,Minghui Zhang,Yanjie Zhu,Qiegen Liu 대한자기공명의과학회 2020 Investigative Magnetic Resonance Imaging Vol.24 No.4
Recently, unsupervised deep learning methods have shown great potential in image processing. Compared with a large-amount demand for paired training data of supervised methods with a specific task, unsupervised methods can learn a universal and explicit prior information on data distribution and integrate it into the reconstruction process. Therefore, it can be used in various image reconstruction environments without showing degraded performance. The importance of unsupervised learning in MRI reconstruction appears to be growing. Nevertheless, the establishment of prior formulation in unsupervised deep learning varies a lot depending on mathematical approximation and network architectures. In this work, we summarized basic concepts of unsupervised deep learning comprehensively and compared performances of several state-of-the-art unsupervised learning methods for MRI reconstruction.
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