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Zhang Y, Yuan L, Zhu Q, Wu J, Nöbauer T, Zhang R, Xiao G, Wang M, Xie H, Guo Z, Dai Q, Vaziri A. A miniaturized mesoscope for the large-scale single-neuron-resolved imaging of neuronal activity in freely behaving mice. Nat Biomed Eng 2024; 8:754-774. [PMID: 38902522 DOI: 10.1038/s41551-024-01226-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/03/2024] [Indexed: 06/22/2024]
Abstract
Exploring the relationship between neuronal dynamics and ethologically relevant behaviour involves recording neuronal-population activity using technologies that are compatible with unrestricted animal behaviour. However, head-mounted microscopes that accommodate weight limits to allow for free animal behaviour typically compromise field of view, resolution or depth range, and are susceptible to movement-induced artefacts. Here we report a miniaturized head-mounted fluorescent mesoscope that we systematically optimized for calcium imaging at single-neuron resolution, for increased fields of view and depth of field, and for robustness against motion-generated artefacts. Weighing less than 2.5 g, the mesoscope enabled recordings of neuronal-population activity at up to 16 Hz, with 4 μm resolution over 300 μm depth-of-field across a field of view of 3.6 × 3.6 mm2 in the cortex of freely moving mice. We used the mesoscope to record large-scale neuronal-population activity in socially interacting mice during free exploration and during fear-conditioning experiments, and to investigate neurovascular coupling across multiple cortical regions.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY, USA
| | - Lekang Yuan
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
| | - Qiyu Zhu
- School of Medicine, Tsinghua University, Beijing, China
- Tsinghua-Peking Joint Center for Life Sciences, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing, China
| | - Tobias Nöbauer
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY, USA
| | - Rujin Zhang
- Department of Anesthesiology, the First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Guihua Xiao
- Department of Automation, Tsinghua University, Beijing, China
| | - Mingrui Wang
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
| | - Zengcai Guo
- School of Medicine, Tsinghua University, Beijing, China
- Tsinghua-Peking Joint Center for Life Sciences, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY, USA.
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY, USA.
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2
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Deng F, Wan J, Li G, Dong H, Xia X, Wang Y, Li X, Zhuang C, Zheng Y, Liu L, Yan Y, Feng J, Zhao Y, Xie H, Li Y. Improved green and red GRAB sensors for monitoring spatiotemporal serotonin release in vivo. Nat Methods 2024; 21:692-702. [PMID: 38443508 PMCID: PMC11377854 DOI: 10.1038/s41592-024-02188-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 01/19/2024] [Indexed: 03/07/2024]
Abstract
The serotonergic system plays important roles in both physiological and pathological processes, and is a therapeutic target for many psychiatric disorders. Although several genetically encoded GFP-based serotonin (5-HT) sensors were recently developed, their sensitivities and spectral profiles are relatively limited. To overcome these limitations, we optimized green fluorescent G-protein-coupled receptor (GPCR)-activation-based 5-HT (GRAB5-HT) sensors and developed a red fluorescent GRAB5-HT sensor. These sensors exhibit excellent cell surface trafficking and high specificity, sensitivity and spatiotemporal resolution, making them suitable for monitoring 5-HT dynamics in vivo. Besides recording subcortical 5-HT release in freely moving mice, we observed both uniform and gradient 5-HT release in the mouse dorsal cortex with mesoscopic imaging. Finally, we performed dual-color imaging and observed seizure-induced waves of 5-HT release throughout the cortex following calcium and endocannabinoid waves. In summary, these 5-HT sensors can offer valuable insights regarding the serotonergic system in both health and disease.
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Affiliation(s)
- Fei Deng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jinxia Wan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Guochuan Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Hui Dong
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Chaowei Zhuang
- Department of Automation, Tsinghua University, Beijing, China
| | - Yu Zheng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Laixin Liu
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yuqi Yan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiesi Feng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Yulin Zhao
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China.
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China.
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
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Mo F, Kong F, Yang G, Xu Z, Liang W, Liu J, Zhang K, Liu Y, Lv S, Han M, Wang Y, Song Y, Wang M, Wu Y, Cai X. Integrated Three-Electrode Dual-Mode Detection Chip for Place Cell Analysis: Dopamine Facilitates the Role of Place Cells in Encoding Spatial Locations of Novel Environments and Rewards. ACS Sens 2023; 8:4765-4773. [PMID: 38015643 DOI: 10.1021/acssensors.3c01864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The functioning of place cells requires the involvement of multiple neurotransmitters, with dopamine playing a critical role in hippocampal place cell activity. However, the exact mechanisms through which dopamine influences place cell activity remain largely unknown. Herein, we present the development of the integrated three-electrode dual-mode detection chip (ITDDC), which enables simultaneous recording of the place cell activity and dopamine concentration fluctuation. The working electrode, reference electrode, and counter electrode are all integrated within the ITDDC in electrochemical detection, enabling the real-time in situ monitoring of dopamine concentrations in animals in motion. The reference, working, and counter electrodes are surface-modified using PtNPs and polypyrrole, PtNPs and PEDOT:PSS, and PtNPs, respectively. This modification allows for the detection of dopamine concentrations as low as 20 nM. We conducted dual-mode testing on mice in a novel environment and an environment with food rewards. We found distinct dopamine concentration variations along different paths within a novel environment, implying that different dopamine levels may contribute to spatial memory. Moreover, environmental food rewards elevate dopamine significantly, followed by the intense firing of reward place cells, suggesting a crucial role of dopamine in facilitating the encoding of reward-associated locations in animals. The real-time and in situ recording capabilities of ITDDC offer new opportunities to investigate the interplay between electrophysiology and dopamine during animal exploration and reward-based memory and provide a novel glimpse into the correlation between dopamine levels and place cell activity.
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Affiliation(s)
- Fan Mo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Fanli Kong
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Gucheng Yang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Juntao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Kui Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Shiya Lv
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Meiqi Han
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Yilin Song
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Yirong Wu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China
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4
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Zhang Y, Zhang G, Han X, Wu J, Li Z, Li X, Xiao G, Xie H, Fang L, Dai Q. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nat Methods 2023; 20:747-754. [PMID: 37002377 PMCID: PMC10172132 DOI: 10.1038/s41592-023-01838-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/07/2023] [Indexed: 04/03/2023]
Abstract
Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Guoxun Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Xiaofei Han
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Ziwei Li
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xinyang Li
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Guihua Xiao
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Lu Fang
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China.
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Zhang R, Zhuang C, Wang Z, Xiao G, Chen K, Li H, Tong L, Mi W, Xie H, Cao J. Simultaneous Observation of Mouse Cortical and Hippocampal Neural Dynamics under Anesthesia through a Cranial Microprism Window. BIOSENSORS 2022; 12:bios12080567. [PMID: 35892463 PMCID: PMC9332076 DOI: 10.3390/bios12080567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/20/2022] [Accepted: 07/24/2022] [Indexed: 11/17/2022]
Abstract
The fluorescence microscope has been widely used to explore dynamic processes in vivo in mouse brains, with advantages of a large field-of-view and high spatiotemporal resolution. However, owing to background light and tissue scattering, the single-photon wide-field microscope fails to record dynamic neural activities in the deep brain. To achieve simultaneous imaging of deep-brain regions and the superficial cortex, we combined the extended-field-of-view microscopy previously proposed with a novel prism-based cranial window to provide a longitudinal view. As well as a right-angle microprism for imaging above 1 mm, we also designed a new rectangular-trapezoidal microprism cranial window to extend the depth of observation to 1.5 mm and to reduce brain injury. We validated our method with structural imaging of microglia cells in the superficial cortex and deep-brain regions. We also recorded neuronal activity from the mouse brains in awake and anesthesitized states. The results highlight the great potential of our methods for simultaneous dynamic imaging in the superficial and deep layers of mouse brains.
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Affiliation(s)
- Rujin Zhang
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
| | - Chaowei Zhuang
- Department of Automation, Tsinghua University, Beijing 100084, China; (C.Z.); (G.X.)
| | - Zilin Wang
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
| | - Guihua Xiao
- Department of Automation, Tsinghua University, Beijing 100084, China; (C.Z.); (G.X.)
| | - Kunsha Chen
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
| | - Hao Li
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
| | - Li Tong
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
| | - Weidong Mi
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing 100084, China; (C.Z.); (G.X.)
- Correspondence: (H.X.); (J.C.)
| | - Jiangbei Cao
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; (R.Z.); (Z.W.); (K.C.); (H.L.); (L.T.); (W.M.)
- Correspondence: (H.X.); (J.C.)
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