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9 results on '"Li, Fanshi"'

1. Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis

Author

Li, Fanshi, Wang, Zhihui, Guo, Yifan, Liu, Congcong, Zhu, Yanjie, Zhou, Yihang, Li, Jun, Liang, Dong, and Wang, Haifeng

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Graphics
Abstract

In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance. The following are the paper's main contributions: (a) propose a novel dynamic GCN architecture, which is an end-to-end pipeline for diagnosis of the AD task; (b) the proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph; (c) incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while decreasing the weights of other noise features. Experiments indicate that our model provides flexibility and stability while achieving excellent classification results in AD diagnosis., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2023

2. Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields

Author

Xie, Taofeng, Cao, Chentao, Cui, Zhuoxu, Li, Fanshi, Wei, Zidong, Zhu, Yanjie, Li, Ye, Liang, Dong, Jin, Qiyu, Chen, Guoqing, and Wang, Haifeng

Subjects
Computer Science - Multimedia, 68U10, J.3
Abstract

MRI and PET are important modalities and can provide complementary information for the diagnosis of brain diseases because MRI can provide structural information of brain and PET can obtain functional information of brain. However, PET is usually missing. Especially, simultaneous PET and MRI imaging at ultrahigh field is not achievable in the current. Thus, synthetic PET using MRI at ultrahigh field is essential. In this paper, we synthetic PET using MRI as a guide by joint probability distribution of diffusion model (JPDDM). Meanwhile, We utilized our model in Ultrahigh Fields., Comment: 5 pages, 4 figures

Published
2022

6. Combining Ligand Deuteration with Ligand Bulkiness in Non-Heme Iron Oxidation Catalysis: Enhancing Catalyst Lifetime and Site-Selectivity

Author

Structural Biochemistry, Organic Chemistry and Catalysis, Sub Organic Chemistry and Catalysis, Sub Structural Biochemistry, Li, Fanshi, Meijer, Isabelle, Kniestedt, Bauke, Lutz, Martin, Broere, Daniël l. j., Klein gebbink, Robertus j. m., Structural Biochemistry, Organic Chemistry and Catalysis, Sub Organic Chemistry and Catalysis, Sub Structural Biochemistry, Li, Fanshi, Meijer, Isabelle, Kniestedt, Bauke, Lutz, Martin, Broere, Daniël l. j., and Klein gebbink, Robertus j. m.

Published
2023

7. Exploration of highly electron-rich manganese complexes in enantioselective oxidation catalysis; a focus on enantioselective benzylic oxidation

Author

Masferrer-rius, Eduard, Li, Fanshi, Lutz, Martin, Klein Gebbink, Robertus J. M., Sub Organic Chemistry and Catalysis, Sub Structural Biochemistry, Organic Chemistry and Catalysis, Structural Biochemistry, Sub Organic Chemistry and Catalysis, Sub Structural Biochemistry, Organic Chemistry and Catalysis, and Structural Biochemistry

Subjects
chemistry.chemical_classification, biology, 010405 organic chemistry, Chemistry, Carboxylic acid, Enantioselective synthesis, Active site, chemistry.chemical_element, Epoxide, Manganese, 010402 general chemistry, 01 natural sciences, Redox, Combinatorial chemistry, Catalysis, 0104 chemical sciences, chemistry.chemical_compound, Yield (chemistry), biology.protein
Abstract

The direct enantioselective hydroxylation of benzylic C–H bonds to form chiral benzylic alcohols represents a challenging transformation. Herein, we report on the exploration of new biologically inspired manganese and iron complexes bearing highly electron-rich aminopyridine ligands containing 4-pyrrolidinopyridine moieties ((S,S)-1, (R,R)-1, 2 and 5) in combination with chiral bis-pyrrolidine and N,N-cyclohexanediamine backbones in enantioselective oxidation catalysis with aqueous H2O2. The current manganese complexes outperform the analogous manganese complexes containing 4-dimethylaminopyridine moieties (3 and 4) in benzylic oxidation reactions in terms of alcohol yield while keeping similar ee values (~ 60% ee), which is attributed to the higher basicity of the 4-pyrrolidinopyridine group. A detailed investigation of different carboxylic acid additives in enantioselective benzylic oxidation provides new insights into how to rationally enhance enantioselectivities by means of proper tuning of the environment around the catalytic active site, and has resulted in the selection of Boc-L-tert-leucine as the preferred additive. Using these optimized conditions, manganese complex 2 was shown to be effective in the enantioselective benzylic oxidation of a series of arylalkane substrates with up to 50% alcohol yield and 62% product ee. A final set of experiments also highlights the use of the new 4-pyrrolidinopyridine-based complexes in the asymmetric epoxidation of olefins (up to 98% epoxide yield and >99% ee).

Published
2021

8. Exploration of highly electron-rich manganese complexes in enantioselective oxidation catalysis; a focus on enantioselective benzylic oxidation

Author

Sub Organic Chemistry and Catalysis, Sub Structural Biochemistry, Organic Chemistry and Catalysis, Structural Biochemistry, Masferrer-rius, Eduard, Li, Fanshi, Lutz, Martin, Klein Gebbink, Robertus J. M., Sub Organic Chemistry and Catalysis, Sub Structural Biochemistry, Organic Chemistry and Catalysis, Structural Biochemistry, Masferrer-rius, Eduard, Li, Fanshi, Lutz, Martin, and Klein Gebbink, Robertus J. M.

Published
2021

9. Developing a Dynamic Graph Network for Interpretable Analysis of Multi-Modal MRI Data in Parkinson's Disease Diagnosis.

Author

Li F, Wang Z, Guo Y, Liu C, Zhu Y, Zhou Y, Li J, Liang D, and Wang H

Subjects
Humans, Diagnosis, Computer-Assisted, Magnetic Resonance Imaging, Algorithms, Parkinson Disease diagnostic imaging
Abstract

Following the aging of the population, Parkinson's disease (PD) poses a severe challenge to public health. For the diagnosis of PD and the prediction of its progression, numerous computer-aided diagnosis procedures have been developed. Recently, Graph Convolutional Networks (GCN) are widely applied in deep learning to effectively integrate multi-modal features and model subject correlation. However, many GCNs which are used for node classification build large-scale fixed graph topologies using the entire dataset, which could make them impossible to verify independently. Furthermore, past GCN algorithms would need more interpretability, limiting their real-world applications. In this paper, an Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results. The proposed method can dynamically adjust the graph structure for GCN to better diagnose outcomes by learning the optimal underlying latent graph. Through interpretable feature learning, the proposed network can interpret diagnosis outcomes. The experiments showed that the proposed method increased flexibility while maintaining a high level of classification performance and could be interpretable for PD diagnosis.Clinical Relevance- The proposed method is expected to have good performance in its strong practicability, feasibility, and interpretability for Parkinson's disease diagnosis.

Published
2023
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