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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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A 2-bit graphene encoder based on the plasmon-induced transparency effect and its sensing characteristics

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Results in Physics 2024年 59卷

作者： Zhu, Aijun Li, Zongteng Hou, Weigang Yang, Xinghua Cheng, Lei Hu, Cong Qiao, Fei Mahapatra, Rabi Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin 541004 China Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems Jincheng Shanxi 048000 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing 400000 China College of Science Beijing Forestry University Beijing 100083 China Jincheng Research Institute of Opto-mechatronics Industry Jincheng Shanxi 048000 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin 541004 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Texas A&M University College Station 77843-3112 TX United States

A 2-bit encoder based on the plasmon-induced transparency (PIT) effect was proposed and investigated in the range from 2 to 7 THz. The Lorentz oscillation coupling model was utilized to confirm the simulation. By tuning the Fermi levels of cross1 and cross2, the 2-bit encoder was achieved. And the minimum MD was 94.73 %, the minimum ER was 12.77 dB, and the maximum IL was 0.32 dB. As a sensor, the structure had a sensitivity of up to 2.33 THz/RIU. In addition, due to the insensitivities to incidence angle and polarization angle, the encoder could be utilized to complex environments. Therefore, these results reveals that our work is significant in terahertz encoders, optical switches, sensors, slow light devices, and modulators. © 2024 The Author(s)

关键词： 2-bit encoder Metamaterial Plasmon-induced transparency (PIT) effect Plasmonic-graphene Terahertz (THz)

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Tripletunet: Multi-Task u-net with online voxel-wise learning for precise ct prostate segmentation

arXiv

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arXiv 2020年

作者： He, Kelei Lian, Chunfeng Adeli, Ehsan Huo, Jing Shi, Yinghuan Gao, Yang Zhang, Bing Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China Department of Radiology Nanjing Drum Tower Hospital Nanjing University Medical School China Department of Radiology BRIC University of North Carolina Chapel HillNC United States Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Department of Psychiatry and Behavioral Sciences Department of Computer Science Stanford University CA United States

Fully convolutional networks (FCN), including U-Net and V-Net, are widely-used network architecture for semantic segmentation in recent studies. However, conventional FCNs are typically trained by the cross-entropy or dice loss, in which the relationships among voxels are neglected. This often results in non-smooth neighborhoods in the output segmentation map. This problem becomes more serious in CT prostate segmentation as CT images are usually of low tissue contrast. To address this problem, we propose a two-stage framework. The first stage quickly localizes the prostate region. Then, the second stage precisely segments the prostate by a multi-task FCN-based on the U-Net architecture. We introduce a novel online voxel-triplet learning module through metric learning and voxel feature embeddings in the multi-task network. The proposed network has two branches guided by two tasks: 1) a segmentation sub-network aiming to generate prostate segmentations, and 2) a triplet learning sub-network aiming to improve the quality of the learned feature space supervised by a mixed of triplet and pair-wise loss function. The triplet learning sub-network samples triplets from the inter-mediate heatmap. Unlike conventional deep triplet learning methods that generate triplets before the training phase, our proposed voxel-triplets are sampled in an online manner and operates in an end-to-end fashion via multi-task learning. To evaluate the proposed method, we implement comprehensive experiments on a CT image dataset consisting of 339 patients. The ablation studies show that our method can effectively learn more representative voxel-level features compared with the conventional FCN network. And the comparisons show that the proposed method outperforms the state-of-the-art methods by a large margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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Mapping global lake dynamics reveals the emerging roles of small lakes (October, 10.1038/s41467-022-33239-3, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-12页

作者： Pi, Xuehui Luo, Qiuqi Feng, Lian Xu, Yang Tang, Jing Liang, Xiuyu Ma, Enze Cheng, Ran Fensholt, Rasmus Brandt, Martin Cai, Xiaobin Gibson, Luke Liu, Junguo Zheng, Chunmiao Li, Weifeng Bryan, Brett A. Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) The University of Hong Kong Department of Urban Planning and Design Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) The University of Hong Kong Urban Systems Institute Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Biology Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Lund University Department of Physical Geography and Ecosystem Science Lund Sweden (GRID:grid.4514.4) (ISNI:0000 0001 0930 2361) Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Chinese Academy of Sciences Institute of Geodesy and Geophysics Wuhan China (GRID:grid.9227.e) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) North China University of Water Resources and Electric Power School of Water Conservancy Zhengzhou China (GRID:grid.412224.3) (ISNI:0000 0004 1759 6955) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263

Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km2, and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km2) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon *** are essential components of the hydrological and biogeochemical cycles. Here, Pi et al develop a global lake dataset called GLAKES via high-resolution satellite images and deep learning to examine global lake changes over four decades.

关键词： Hydrology Limnology

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Efficient T2 mapping with Blip-up/down EPI and gSlider-SMS (T2-BUDA-gSlider)

arXiv

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arXiv 2019年

作者： Cao, Xiaozhi Liao, Congyu Zhang, Zijing Iyer, Siddharth Srinivasan Chen, Zhifeng Lo, Wei-Ching Liu, Huafeng Wang, Kang He, Hongjian Setsompop, Kawin Zhong, Jianhui Bilgic, Berkin Center for Brain Imaging Science and Technology Department of Biomedical Engineering Zhejiang University Zhejiang Hangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China Siemens Medical Solutions BostonMA United States Department of Neurology The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Harvard-MIT Department of Health Sciences and Technology CambridgeMA United States Department of Imaging Sciences University of Rochester NY United States

Purpose: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down echo planar imaging (EPI) acquisition with generalized Slice-dithered enhanced resolution (T2-BUDA-gSlider) is proposed. A radiofrequency (RF)-encoded multi-slab spin-echo EPI acquisition with multiple echo times (TEs) was developed to obtain high SNR efficiency with reduced repetition time (TR). This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and utilized for T2 quantification. Results: In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated. Conclusion: BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Spin polarization

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Identification of cancer-keeping genes as therapeutic targets by finding network control hubs

arXiv

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arXiv 2022年

作者： Zhang, Xizhe Pan, Chunyu Wei, Xinru Yu, Meng Liu, Shuangjie An, Jun Yang, Jieping Wei, Baojun Hao, Wenjun Yao, Yang Zhu, Yuyan Zhang, Weixiong Early Intervention Unit Department of Psychiatry Affiliated Nanjing Brain Hospital Nanjing Medical University Nanjing China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Department of Laboratory Animal Science China Medical University Shenyang China Key Laboratory of Transgenetic Animal Research China Medical University Shenyang China Department of Urology First Affiliated Hospital of China Medical University Shenyang China Department of Physiology Shenyang Medical College Shenyang China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Department of Genetics Washington University in St. Louis St. LouisMO United States

Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state. While theoretically sound, this approach is impractical for many reasons, e.g., the control scheme is often not unique and half of the nodes may be driver genes for the cell. We developed a novel approach that transcends structural controllability. Instead of considering driver genes for one control scheme, we considered control hub genes that reside in the middle of a control path of every control scheme. Control hubs are the most vulnerable spots for controlling the cell and exogenous stimuli on them may render the cell uncontrollable. We adopted control hubs as cancer-keep genes (CKGs) and applied them to a gene regulatory network of bladder cancer (BLCA). All the genes on the cell cycle and p53 singling pathways in BLCA are CKGs, confirming the importance of these genes and the two pathways in cancer. A smaller set of 35 sensitive CKGs (sCKGs) for BLCA was identified by removing network links. Six sCKGs (RPS6KA3, FGFR3, N-cadherin (CDH2), EP300, caspase-1, and FN1) were subjected to small-interferencing-RNA knockdown in four cell lines to validate their effects on the proliferation or migration of cancer cells. Knocking down RPS6KA3 in a mouse model of BLCA significantly inhibited the growth of tumor xenografts in the mouse model. Combined, our results demonstrated the value of CKGs as therapeutic targets for cancer therapy and the potential of CKGs as an effective means for studying and characterizing cancer etiology. Copyright © 2022, The Authors. All rights reserved.

关键词： Genes

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Using deep learning models to examine the biological impact of polygenic risks for Alzheimer’s disease

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Alzheimer's & Dementia 2023年第S1期19卷

作者： Xiaopu Zhou Fanny C. F. Ip Ge Lv Yuanbing JIANG Han Cao Huan Zhong Lei Chen Amy K.Y. Fu Nancy Y. Ip Division of Life Science State Key Laboratory of Molecular Neuroscience Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China Guangdong Provincial Key Laboratory of Brain Science Disease and Drug Development HKUST Shenzhen Research Institute Shenzhen–Hong Kong Institute of Brain Science Shenzhen Guangdong 518057 China Hong Kong Center for Neurodegenerative Diseases Hong Kong Science Park Hong Kong China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China

Background Alzheimer’s disease (AD) is a leading cause of mortality in the elderly. Genetics studies suggest that AD is polygenic in nature; thus, an individual’s risk of AD can be predicted by polygenic risk score (PRS) models. Of these models, the deep learning models more accurately capture nonlinear interactions among different genetic risks and are more suitable for modeling disease polygenic risk. Method We used the neural network-based deep learning models to evaluate the polygenic risk of AD. Meanwhile, we examined the associations of resultant PRSs and plasma protein biomarkers for AD by regression analysis. We also identified the biological pathways that contribute to the polygenic risk using Gene Set Enrichment Analysis. Result We found that the resultant PRSs of AD are associated with the levels of specific AD plasma protein biomarkers, including amyloid beta and p-tau181, as well as changes in the activity of specific biological events, such as TNF-α and cytokine-related pathways. Conclusion Our findings suggest that AD polygenic risk is associated with AD progression and modulates immune-associated pathways. They also highlight the potential use of deep learning methods in investigating the underlying genetic mechanisms of AD.

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Recommendations for the diagnosis and treatment of paroxysmal kinesigemc dyskinesia: an expert consensus in China

引用

Translational Neurodegeneration 2021年第1期10卷 67-76页

作者： Li Cao Xiaojun Huang Ning Wang Zhiying Wu Cheng Zhang Weihong Gu Shuyan Cong Jianhua Ma Ling Wei Yanchun Deng Qi Fang Qi Niu Jin Wang Zhaoxia Wang You Yin Jinyong Tian Shufen Tian Hongyan Bi Hong Jiang Xiaorong Liu Yang Lu Meizhen Sun Jianjun Wu Erhe Xu Tao Chen Tao Chen Xu Chen Wei Li Shujian Li Qinghua Li Xiaonan Song Ying Tang Ping Yang Yun Yang Min Zhang Xiong Zhang Yuhu Zhang Ruxu Zhang Yi Ouyang Jintai Yu Quanzhong Hu Qing Ke Yuanrong Yao Zhe Zhao Xiuhe Zhao Guohua Zhao Furu Liang Nan Cheng Jianhong Han Rong Peng Shengdi Chen Beisha Tang Department of Neurology Ruijin Hospital Shanghai Jiao Tong University School of MedicineShanghaiChina Department of Neurology The Sixth People's Hospital Shanghai Jiaotong UniversityShanghaiChina Department of Neurology and Institute of Neurology The First Affiliated HospitalFujian Medical University/Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhouChina Department of Neurology and Research Center of Neurology The Second Affiliated Hospital/Key Laboratory of Medical Neurobiology of Zhejiang ProvinceZhejiang University School of MedicineHangzhouChina Department of Neurology The First Affiliated HospitalSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases/National Key Clinical Department and Key Discipline of NeurologyGuangzhouChina Department of Neurology China-Japan Friendship HospitalMovement Disorder and Neurogenetics Research CenterBeijingChina Department of Neurology Shengjing Hospital of China Medical UniversityShenyangChina Department of Neurology The First Affiliated Hospital of Xinjiang Medical UniversityUrumqiChina Department of Neurology The First Affiliated Hospital of Anhui Medical UniversityCollaborative Innovation Center for Neuropsychiatric Disorders and Mental HealthAnhui Medical University/Anhui Province Key Laboratory of Cognition and Neuropsychiatry DisordersHefeiChina Department of Neurology Xijing HospitalFourth Military Medical UniversityXi'anChina Department of Neurology The First Affiliated Hospital of Soochow UniversitySuzhouChina Department of Neurology The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina Department of Neurology The First Affiliated Hospital of Guangxi Medical UniversityNanningChina Department of Neurology Peking University First HospitalBeijingChina Department of Neurology Changzheng Hospitalthe PLA Naval Medical UniversityShanghaiChina Department of Neurology and Department of Emergency Guizhou Provincial People's Hospital

Paroxysmal dyskinesias are a group of neurological diseases characterized by intermittent episodes of involuntary movements with different *** kinesigenic dyskinesia(PKD)is the most common type of paroxysmal dyskinesia and can be divided into primary and secondary types based on the ***,PKD is characterized by recurrent and transient attacks of involuntary movements precipitated by a sudden voluntary *** major cause of primary PKD is genetic abnormalities,and the inheritance pattern of PKD is mainly autosomal-dominant with incomplete *** proline-rich transmembrane protein 2(PRRT2)was the first identified causative gene of PKD,accounting for the majority of PKD cases *** increasing number of studies has revealed the clinical and genetic characteristics,as well as the underlying mechanisms of *** seeking the views of domestic experts,we propose an expert consensus regarding the diagnosis and treatment of PKD to help establish standardized clinical evaluation and therapies for *** this consensus,we review the clinical manifestations,etiology,clinical diagnostic criteria and therapeutic recommendations for PKD,and results of genetic analyses in PKD patients performed in domestic hospitals.

关键词： Paroxysmal kinesigenic dyskinesia Diagnosis and treatment Expert consensus China

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En route for molecular dynamics simulation of a living cell

引用

Fundamental Research 2022年

作者： Yibo Wang Cong Zhang Ke Tang Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin 130022 China State Key Laboratory of Natural and Biomimetic Drugs Peking University Beijing China School of Applied Chemistry and Engineering University of Science and Technology of China Hefei Anhui 230026 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Beijing National Laboratory for Molecular Sciences Beijing 100190 China

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