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Twisted-Planar Molecular Engineering with Sonication-Induced J-Aggregation To Design Near-Infrared J-Aggregates for Enhanced Phototherapy

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Angewandte Chemie 2024年第7期137卷

作者： Yubo Liu Yuchen Song Dr. Zhong-Hong Zhu Chao Ji Jianqing Li Hanyu Jia Prof. Yang Shi Prof. Fang Hu Prof. Zujin Zhao Prof. Dan Ding Prof. Ben Zhong Tang Prof. Guangxue Feng State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Luminescence from Molecular Aggregates School of Materials Science and Engineering South China University of Technology Guangzhou 510640 China These authors contributed equally to this work Biomaterials Research Center School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou 510515 China State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Bioactive Materials Ministry of Education and College of Life Sciences Nankai University Tianjin 300071 China School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong Shenzhen Shenzhen 518172 China

J-aggregates show great promise in phototherapy, but are limited to specific molecular skeletons and poor molecular self-assembly controllability. Herein, we report a twisted-planar molecular strategy with sonication-induced J-aggregation to develop donor–acceptor (D–A) type J-aggregates for phototherapy. With propeller aggregation-induced emission (AIE) moieties as the twisted subunits and thiophene as the planar π-bridge, the optimal twisted-planar π-interaction in MTSIC induces appropriate slip angle and J-aggregates formation, redshifting the absorption from 624 nm to 790 nm. In contrast, shorter π-planarity results in amorphous aggregates, and elongation promotes charge transfer (CT) coupled J-aggregates. Sonication was demonstrated to be effective in controlling self-assembly behaviors of MTSIC, which enables the transformation from amorphous aggregates to H-intermediates, and finally to stable J-aggregates. After encapsulation with lipid-PEG, the resultant J-dots show enhanced phototherapeutic effects over amorphous dots, including brightness, reactive oxygen species (ROS) generation, and photothermal conversion, delivering superior cancer phototherapy performance. This work not only advances D–A type J-aggregates design but also provides a promising strategy for supramolecular assembly development.

关键词： Self-assembly J-aggregation type I ROS donor-π-acceptor small molecules phototherapy

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A Novel LOS Rate Estimation Method Based on images for Strap-down Inertial Guidance

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Journal of Physics: Conference Series 2020年第1期1570卷

作者： Zheng Xu Haibo Luo Bin Hui Zheng Chang Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Tel.: +86-024-2397-0757 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science Shenyang 110016 China The Key Lab of Image Understanding and Computer Vision Shenyang 110016 China

With the development of technology, precision guided weapon is becoming more and more important in modern war. In order to launch our recent guidance system on medium and small guided weapons, we propose a method to obtain the LOS rate by combining information from both camera and gyroscope. To be specific, we firstly calculate the body LOS angle through transforming the image pixel coordinate system into the image physical coordinate system according to camera internal parameters; then subtract the missile motion information contained in the measurement signal of the seeker and finally the LOS rate is deduced. Comparing with traditional gimballed seekers, our strap-down seekers with camera have significantly reduced costs and influences caused by external environments on platform.

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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SU-D-206-07: CBCT Scatter Correction Based On Rotating Collimator

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Medical Physics 2016年第6PART4期43卷 3346-3346页

作者： G Yu Y Yin L Qiang B Li Z Feng P Huang D Li Shandong Normal University Jinan Shandong Shandong Cancer Hospital and Institute China Jinan Shandong Zhang Jiagang STFK Medical Device Co Zhangjiangkang Suzhou Shandong Academy of Medical Sciences Jinan Shandong provice Shandong Province Key Laboratory of Medical Physics and Image Processing Te Ji'nan Shandong province School of Physics and Electronics Shandong Normal University Jinan Shandong

Purpose: Scatter correction in cone-beam computed tomography (CBCT) has obvious effect on the removal of image noise, the cup artifact and the increase of image contrast. Several methods using a beam blocker for the estimation and subtraction of scatter have been proposed. However, the inconvenience of mechanics and propensity to residual artifacts limited the further evolution of basic and clinical research. Here, we propose a rotating collimator-based approach, in conjunction with reconstruction based on a discrete Radon transform and Tchebichef moments algorithm, to correct scatter-induced artifacts. Methods: A rotating-collimator, comprising round tungsten alloy strips, was mounted on a linear actuator. The rotating-collimator is divided into 6 portions equally. The round strips space is evenly spaced on each portion but staggered between different portions. A step motor connected to the rotating collimator drove the blocker to around x-ray source during the CBCT acquisition. The CBCT reconstruction based on a discrete Radon transform and Tchebichef moments algorithm is performed. Experimental studies using water phantom and Catphan504 were carried out to evaluate the performance of the proposed scheme. Results: The proposed algorithm was tested on both the Monte Carlo simulation and actual experiments with the Catphan504 phantom. From the simulation result, the mean square error of the reconstruction error decreases from 16% to 1.18%, the cupping (τcup) from 14.005% to 0.66%, and the peak signal-to-noise ratio increase from 16.9594 to 31.45. From the actual experiments, the induced visual artifacts are significantly reduced. Conclusion: We conducted an experiment on CBCT imaging system with a rotating collimator to develop and optimize x-ray scatter control and reduction technique. The proposed method is attractive in applications where a high CBCT image quality is critical, for example, dose calculation in adaptive radiation therapy. We want to thank Dr. Lei X

关键词： Cone beam computed tomography X-ray scattering Collimators Medical image artifacts Medical X-ray imaging Medical image noise Medical image contrast Monte Carlo methods Tungsten

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Two-dimensional picture arrays and Parikh q—matrices

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Journal of Physics: Conference Series 2018年第1期1132卷

作者： Somnath Bera Kalpana Mahalingam Linqiang Pan K. G. Subramanian Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Department of Mathematics Indian Institute of Technology Madras Chennai 600036 India School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 Henan China Department of Mathematics Madras Christian college Tambaram Chennai 600017 India

Based on the idea of 'q—count' of certain subwords of a word and generalizing the notion of Parikh matrix of a word, the notion of Parikh q—matrix of a word over an ordered alphabet was introduced. On the other hand, with a two-dimensional picture array of symbols arranged in rows and columns, two kinds of upper triangular matrices, known as row and column Parikh matrices have also been introduced and investigated. Here combining these two kinds of matrices of a picture array, we introduce row/column Parikh q—matrix of an array, leading to the concept of q—ambiguity of a picture array. Results relating to q—ambiguity of picture arrays are derived in the context of these Parikh q—matrices of arrays.

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A FAST RADIO BURST DISCOVERED IN FAST DRIFT SCAN SURVEY

arXiv

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arXiv 2020年

作者： Zhu, Weiwei Li, Di Luo, Rui Miao, Chenchen Zhang, Bing Spitler, Laura Lorimer, Duncan Kramer, Michael Champion, David Yue, Youling Cameron, Andrew Cruces, Marilyn Duan, Ran Feng, Yi Han, Jun Hobbs, George Niu, Chenhui Niu, Jiarui Pan, Zhichen Qian, Lei Shi, Dai Tang, Ningyu Wang, Pei Wang, Hongfeng Yuan, Mao Zhang, Lei Zhang, Xinxin Cao, Shuyun Feng, Li Gan, Hengqian Gao, Long Gu, Xuedong Guo, Minglei Hao, Qiaoli Huang, Lin Huang, Menglin Jiang, Peng Jin, Chengjin Li, Hui Li, Qi Li, Qisheng Liu, Hongfei Pan, Gaofeng Peng, Bo Qian, Hui Shi, Xiangwei Song, Jinyuo Song, Liqiang Sun, Caihong Sun, Jinghai Wang, Hong Wang, Qiming Wang, Yi Xie, Xiaoyao Yan, Jun Yang, Li Yang, Shimo Yao, Rui Yu, Dongjun Yu, Jinglong Zhang, Chengmin Zhang, Haiyan Zhang, Shuxin Zheng, Xiaonian Zhou, Aiying Zhu, Boqin Zhu, Lichun Zhu, Ming Zhu, Wenbai Zhu, Yan CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Department of Physics and Astronomy University of Nevada Las Vegas Las VegasNV89154 United States National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Max-Planck-Institut fr Radioastronomie Auf dem Hgel 69 BonnD-53121 Germany Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology Chestnut Ridge Research Building MorgantownWV26505 United States Image Processing and Pattern Recognition Laboratory School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Information Management Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China

We report the discovery of a highly dispersed fast radio burst, FRB 181123, from an analysis of ∼1500 hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0–1.5 GHz observing band. We measure the peak flux density to be > 0.065 Jy and the corresponding fluence > 0.2 Jy ms. Based on the observed dispersion measure of 1812 cm−3 pc, we infer a redshift of ∼ 1.9. From this, we estimate the peak luminosity and isotropic energy to be . 2 × 1043 erg s−1 and . 2 × 1040 erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences > 0.025 Jy ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Multi-expert ensemble ECG diagnostic algorithm using mutually exclusive–symbiotic correlation between 254 hierarchical multiple labels

npj Cardiovascular Health

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npj Cardiovascular Health 2024年第1期1卷 1-13页

作者： Jiewei Lai Yue Zhang Chenyu Zhao Qianjin Feng Wei Yang Jinliang Wang Yong Yan Mingyang Chen Lei Ji Jun Guo Baoshi Han Yajun Shi Yundai Chen Jinxia Zhang School of Biomedical Engineering Southern Medical University Guangzhou China Guangdong Provincial Key Laboratory of Medical Image Processing Guangzhou China CardioCloud Medical Technology (Beijing) Co. Ltd. Beijing China IT Department Chinese PLA General Hospital Beijing China Department of Cardiology Chinese PLA General Hospital Beijing China Department of Cardiology General Hospital of Southern Theatre Command of PLA Guangzhou China

Electrocardiograms (ECGs) are a cheap and convenient means of assessing heart health and provide an important basis for diagnosis and treatment by cardiologists. However, existing intelligent ECG diagnostic approaches can only detect up to several tens of ECG terms, which barely cover the most common arrhythmias. Thus, further diagnosis is required by cardiologists in clinical settings. This paper describes the development of a multi-expert ensemble learning model that can recognize 254 ECG terms. Based on data from 191,804 wearable 12-lead ECGs, mutually exclusive–symbiotic correlations between hierarchical multiple labels are applied at the loss level to improve the diagnostic performance of the model and make its predictions more reasonable while alleviating the difficulty of class imbalance. The model achieves an average area under the receiver operating characteristics curve of 0.973 and 0.956 on offline and online test sets, respectively. We select 130 terms from the 254 available for clinical settings by considering the classification performance and clinical significance, providing real-time and comprehensive ancillary support for the public.

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NTIRE 2021 Challenge on image Deblurring

arXiv

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： image enhancement

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3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

arXiv

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arXiv 2022年

作者： Chen, Zhifeng Liao, Congyu Cao, Xiaozhi Poser, Benedikt A. Xu, Zhongbiao Lo, Wei-Ching Wen, Manyi Cho, Jaejin Tian, Qiyuan Wang, Yaohui Feng, Yanqiu Xia, Ling Chen, Wufan Liu, Feng Bilgic, Berkin School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Radiology Stanford University Stanford CA United States Maastricht Brain Imaging Center Faculty of Psychology and Neuroscience University of Maastricht Netherlands Department of Radiotherapy Cancer Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Science Guangzhou China Siemens Medical Solutions BostonMA United States Department of Chemical Pathology The Chinese University of Hong Kong Hong Kong Division of Superconducting Magnet Science and Technology Institute of Electrical Engineering Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1×1.1×1.0 mm3 resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Cross-chain mapping blockchain: scalable data management in massive IoT networks

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Digital communications and Networks 2024年

作者： Wenjian Hu Yao Yu Xin Hao Phee Lep Yeoh Lei Guo Yonghui Li School of Computer Science and Engineering Northeastern University Shenyang 110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110819 China School of Information Technology Deakin University Waurn Ponds VIC 3216 Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs QLD 4556 Australia School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Electrical and Computer Engineering University of Sydney Sydney NSW 2006 Australia

We propose a Cross-Chain Mapping Blockchain (CCMB) for scalable data management in massive Internet of Things (IoT) networks. Specifically, CCMB aims to improve the scalability of securely storing, tracing, and transmitting IoT behavior and reputation data based on our proposed cross-mapped Behavior Chain (BChain) and Reputation Chain (RChain). To improve off-chain IoT data storage scalability, we show that our lightweight CCMB architecture efficiently utilizes available fog-cloud resources. The scalability of on-chain IoT data tracing is enhanced using our Mapping Smart Contract (MSC) and cross-chain mapping design to perform rapid Reputation-to-Behavior (R2B) traceability queries between BChain and RChain blocks. To maximize off-chain to on-chain throughput, we optimize the CCMB block settings and producers based on a general Poisson Point Process (PPP) network model. The constrained optimization problem is formulated as a Markov Decision Process (MDP), and solved using a dual-network Deep Reinforcement Learning (DRL) algorithm. Simulation results validate CCMB's scalability advantages in storage, traceability, and throughput. In specific massive IoT scenarios, CCMB can reduce the storage footprint by 50% and traceability query time by 90%, while improving system throughput by 55% compared to existing benchmarks.

关键词： Blockchain Cross-chain mappings Data management Internet of things Deep reinforcement learning

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