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A multimodal dataset for coronary microvascular disease biomarker discovery

Scientific data 2025年第1期12卷 1-7页

作者： Dantong Li Xiaoting Peng Lianting Hu Xinyang Long Huan Yang Shuai Huang Lingcong Kong Huiying Liang Xueli Zhang Huan Ma Jintai Chen Siting Ye Xiaohe Bai Chao Wu Entao Liu Shuxia Wang Qingshan Geng Medical Big Data Center Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangdong Provincial Cardiovascular Institute Guangzhou Guangdong Province 510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong Province 510080 China Information Hub the Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Ultrasound The Second Affiliated Hospital of Guangzhou University Guangzhou China School of Physical Sciences University of California San Diego La Jolla San Diego CA 92093 USA Institute for Heart and Brain Health University of Michigan Medical Center Ann Arbor Michigan USA Department of Nuclear Medicine Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Cardiology Shenzhen People’s Hospital (The Second Clinical Medical College Jinan University Guangzhou China

Coronary microvascular disease (CMD), particularly prevalent among women, is associated with increased morbidity and mortality, making clinical screening vital for effective management. However, limited publicly available screening-level data hinders disease-specific biomarker discovery. To address this gap, 80 female angina patients without obstructive coronary artery disease and 40 age-matched female controls were prospectively enrolled to curate a new dataset. All participants underwent adenosine stress with electrocardiogram (ECG) monitoring across Rest, Stress, and Recovery stages. CMD diagnosis was confirmed with the standard clinical criterion, i.e., coronary flow reserve (CFR) < 2.0 via PET/CT. Using ECG variables from different stages, we developed machine learning models to classify CMD, thus validating dataset’s effectiveness in CMD identification. We also validated the potential of ECG for differential diagnosis through joint analysis with the published mental stress-induced myocardial ischemia (MSIMI) dataset, which is based on the same cohort under different stress conditions. Disease-specific ECG variable sets were identified. Our findings highlight the value of multi-stage ECG in CMD screening. We expect this dataset to significantly advance CMD research.

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Competitive endogenous RNA is an intrinsic component of EMT regulatory circuits and modulates EMT (vol 10, 1637, 2019)

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NATURE COMMUNICATIONS 2019年第1期10卷 1-12页

作者： Liu, Yuwei Xue, Mengzhu Du, Shaowei Feng, Wanwan Zhang, Ke Zhang, Liwen Liu, Haiyue Jia, Guoyi Wu, Lingshuang Hu, Xin Chen, Luonan Wang, Peng Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of Sciences 200031 Shanghai China University of Chinese Academy of Sciences 200031 Shanghai China School of Life Sciences Shanghai University 200031 Shanghai China Bio-Med Big Data Center CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Shanghai Institutes for Biological Sciences Chinese Academy of Sciences 200031 Shanghai China Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences 200031 Shanghai China Shanghai Institute of Materia Medica Chinese Academy of Sciences 200031 Shanghai China School of Life Science and Technology ShanghaiTech University 201210 Shanghai China Department of Breast Surgery Key Laboratory of Breast Cancer in Shanghai Fudan University Shanghai Cancer Center Fudan University 200032 Shanghai China Precision Cancer Medicine Center Fudan University Shanghai Cancer Center 200032 Shanghai China Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences 650223 Kunming China Research Center for Brain Science and Brain-Inspired Intelligence 201210 Shanghai China

The competitive endogenous RNA (ceRNA) hypothesis suggests an intrinsic mechanism to regulate biological processes. However, whether the dynamic changes of ceRNAs can modulate miRNA activities remains controversial. Here, we examine the dynamics of ceRNAs during TGF-β-induced epithelial-to-mesenchymal transition (EMT). We observe that TGFBI, a transcript highly induced during EMT in A549 cells, acts as the ceRNA for miR-21 to modulate EMT. We further identify FN1 as the ceRNA for miR-200c in the canonical SNAIL-ZEB-miR200 circuit in MCF10A cells. Experimental assays and computational simulations demonstrate that the dynamically induced ceRNAs are directly coupled with the canonical double negative feedback loops and are critical to the induction of EMT. These results help to establish the relevance of ceRNA in cancer EMT and suggest that ceRNA is an intrinsic component of the EMT regulatory circuit and may represent a potential target to disrupt EMT during tumorigenesis.

关键词： Cancer Dynamic networks Gene regulatory networks miRNAs

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Inhibition of NAD-GPx4 axis and MEK triggers ferroptosis to suppress pancreatic ductal adenocarcinoma

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Molecular therapy : the journal of the American Society of Gene Therapy 2025年

作者： Hui Jiang Yusuke Satoh Ryodai Yamamura Takako Ooshio Yang Luo Han Hai Takuya Otsuka Soichiro Hata Reo Sato Taiga Hirata Tsuyoshi Osawa Keisuke Goda Masahiro Sonoshita Division of Biomedical Oncology Institute for Genetic Medicine Hokkaido University Sapporo 060-0815 Japan. Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China. Division of Biomedical Oncology Institute for Genetic Medicine Hokkaido University Sapporo 060-0815 Japan Department of Surgical Pathology Hokkaido University Hospital Sapporo 060-8648 Japan. Division of Integrative Nutriomics and Oncology the Research Center for Advanced Science and Technology The University of Tokyo Tokyo 153-8904 Japan. FlyWorks K.K. Sapporo 060-0062 Japan FlyWorks America Inc. MA 01201 USA Department of Chemistry The University of Tokyo Tokyo 113-0033 Japan Institute of Technological Sciences Wuhan University Hubei 430072 China Department of Bioengineering University of California Los Angeles California 90095 USA. FlyWorks America Inc. MA 01201 USA. Electronic address: msonoshita@igm.hokudai.ac.jp.

Pancreatic ductal adenocarcinoma (PDAC) ranks among the most lethal malignancies, highlighting the critical need for innovative therapeutic strategies. In this study, we examined the roles of nicotinamide adenine dinucleotide (NAD) synthesis pathway in PDAC. Targeting the nicotinamide adenine dinucleotide (NAD) synthesis pathway significantly mitigated lethality in a Drosophila model that recapitulated the PDAC genotype. Within this pathway, we identified Glutathione peroxidase 4 (GPx4) as a critical effector responsible for scavenging reactive oxygen species (ROS). The combined application of GPx4 and Mitogen-activated protein kinase kinase (MEK) inhibitors, namely ML210 and trametinib, respectively, reduced lethality and tumor-like phenotypes in these flies. Notably, this combination treatment synergistically suppressed the proliferation of human PDAC cells and their corresponding xenografts in mice by inducing ROS accumulation, which triggered ferroptosis. These results suggest that inducing ferroptosis could represent a promising therapeutic strategy for PDAC.

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

arXiv

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arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Adaptive neural output consensus control of stochastic nonlinear strict-feedback multi-agent systems

Adaptive neural output consensus control of stochastic nonli...

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2018 Australian and New Zealand Control Conference, ANZCC 2018

作者： Yang, Yang Miao, Songtao Xu, Chuang Yue, Dong Tan, Jie Tian, Yu-Chu College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD4001 Australia

ISBN: (纸本)9781538666173

An adaptive neural output consensus control issue is considered for stochastic nonlinear strict-feedback multi-agent systems (MASs). The traditional backstepping framework is employed combing with the graph theory, as well as neural networks (NNs) technology. NNs are utilized for the approximation of unknown functions, and the Itô's lemma is used to deal with stochastic dynamics of the system. It is proved that all signals remain bounded in probability and that the tracking errors of all followers converge to a small neighborhood of the origin in the sense of mean quartic value by suitable choice of parameters. A simulation example is provided. © 2018 IEEE.

关键词： Multi agent systems

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The Sinusoidal Assisted MEMD based CCA Method for SSVEP based BCI Improvement

The Sinusoidal Assisted MEMD based CCA Method for SSVEP base...

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International Conference on Computational intelligence and Communication Networks (CICN)

作者： Gaopeng Sun Yanhua Shi Hui Liu Yichuan Jiang Pan Lin Junfeng Gao Ruimin Wang Yue Leng Yuankui Yang Sheng Ge Keiji Iramina Ministry of Education Southeast University Nanjing China School of Big Data and Intelligence Technology Huanghe Science and Technology College Zhengzhou China Key Laboratory of Cognitive Science of State Ethnic Affairs Commission South-Central University for Nationalities Wuhan China Graduate School of Systems Life Sciences Kyushu University Motooka Nishi-ku Fukuoka Japan

Although the canonical correlation analysis (CCA) algorithm has been applied successfully to SSVEP detection, artifacts and unrelated brain activities may influence the performance of the steady state visual evoked potential (SSVEP) based brain-computer interfaces (BCI) system. Extracting the characteristic frequency sub-bands is an effective method to enhance the signal-to-noise-ratio of SSVEP signals. The sinusoid-assisted MEMD (SA-MEMD) algorithm is a powerful method for spectral decomposition. In this study, we propose an SA-MEMD based CCA method for SSVEP detection. The results suggest that the SA-MEMD based CCA algorithm is a useful method in the detection of typical SSVEP signals. The classification accuracy achieved 88.3% in a 4 s time window and there was a 2.8% improvement compared with the standard CCA algorithm.

关键词： Correlation Standards Visualization Classification algorithms Time series analysis Electroencephalography Electrodes

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Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification

arXiv

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arXiv 2018年

作者： Guan, Qingji Huang, Yaping Zhong, Zhun Zheng, Zhedong Zheng, Liang Yang, Yi Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Center for Artificial Intelligence University of Technology SydneyNSW2007 Australia Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Cognitive Science Department Xiamen University and Center for Artificial Intelligence University of Technology SydneyNSW2007 Australia

This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN). AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. The comprehensive experiment is conducted on the ChestX-ray14 dataset. We first report a strong global baseline producing an average AUC of 0.841 with ResNet50 as backbone. After combining the local cues with the global information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is used, the average AUC achieves 0.871, which is a new state of the art in the community. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

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Global cross-cultural validation of a brief measure for identifying potential suicide risk in 42 countries

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Public Health 2024年 229卷 13-23页

作者： Gewirtz-Meydan, A. Koós, M. Nagy, L. Kraus, S.W. Demetrovics, Z. Potenza, M.N. Ballester-Arnal, R. Batthyány, D. Bergeron, S. Billieux, J. Burkauskas, J. Cárdenas-López, G. Carvalho, J. Castro-Calvo, J. Chen, L. Ciocca, G. Corazza, O. Csako, R. Fernandez, D.P. Fujiwara, H. Fernandez, E.F. Fuss, J. Gabrhelík, R. Gjoneska, B. Gola, M. Grubbs, J.B. Hashim, H.T. Islam, M.S. Ismail, M. Jiménez-Martínez, M.C. Jurin, T. Kalina, O. Klein, V. Költő, A. Lee, S.-K. Lewczuk, K. Lin, C.-Y. Lochner, C. López-Alvarado, S. Lukavská, K. Mayta-Tristán, P. Miller, D.J. Orosová, O. Orosz, G. Ponce, F.P. Quintana, G.R. Quintero Garzola, G.C. Ramos-Diaz, J. Rigaud, K. Rousseau, A. De Tubino Scanavino, M. Schulmeyer, M.K. Sharan, P. Shibata, M. Shoib, S. Sigre-Leirós, V. Sniewski, L. Spasovski, O. Steibliene, V. Stein, D.J. Strong, C. Ünsal, B.C. Vaillancourt-Morel, M.-P. Van Hout, M.C. Bőthe, B. School of Social Work Faculty of Social Welfare and Health Sciences University of Haifa Israel Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Forensic Psychiatry and Sex Research University of Duisburg-Essen Essen Germany Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Lithuania Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University China Section of Sexual Psychopathology Department of Dynamic and Clinical Psychology and Health Studies Sapienza University of Rome Rome Italy Department of Clinical Pharmaceutical and Biological Sciences University of Hertfordshire United Kingdom Department of Psychology and Cognitive Science University of Trento Italy Department of Psychology and Neuroscience Auckland University of Technology Auckland New Z

Objectives: This study aimed to examine the psychometric properties of the P4 suicide screener in a multinational sample. The primary goal was to evaluate the reliability and validity of the scale and investigate its convergent validity by analyzing its correlation with depression, anxiety, and substance use. Study design: The study design is a cross-sectional self-report study conducted across 42 countries. Methods: A cross-sectional, self-report study was conducted in 42 countries, with a total of 82,243 participants included in the final data set. Results: The study provides an overview of suicide ideation rates across 42 countries and confirms the structural validity of the P4 screener. The findings indicated that sexual and gender minority individuals exhibited higher rates of suicidal ideation. The P4 screener showed adequate reliability, convergence, and discriminant validity, and a cutoff score of 1 is recommended to identify individuals at risk of suicidal behavior. Conclusions: The study supports the reliability and validity of the P4 suicide screener across 42 diverse countries, highlighting the importance of using a cross-cultural suicide risk assessment to standardize the identification of high-risk individuals and tailoring culturally sensitive suicide prevention strategies. © 2024 The Royal Society for Public Health

关键词： Assessment Cross-cultural Suicide Validation

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Deterministic single-photon source in the ultrastrong-coupling regime

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Physical Review A 2023年第3期108卷 L031701-L031701页

作者： Jie Peng Jianing Tang Pinghua Tang Zhongzhou Ren Junlong Tian Nancy Barraza Gabriel Alvarado Barrios Lucas Lamata Enrique Solano F. Albarrán-Arriagada Hunan Key Laboratory for Micro-Nano Energy Materials and Devices and School of Physics and Optoelectronics Xiangtan University Hunan 411105 China School of Physics Science and Engineering Tongji University Shanghai 200092 China Department of Electronic Science College of Big Data and Information Engineering Guizhou University Guiyang 550025 China Kipu Quantum Greifswalderstrasse 226 10405 Berlin Germany Departamento de Física Atómica Molecular y Nuclear Universidad de Sevilla 41080 Sevilla Spain Instituto Carlos I de Física Teórica y Computacional 18071 Granada Spain International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics Shanghai University 200444 Shanghai China IKERBASQUE Basque Foundation for Science Plaza Euskadi 5 48009 Bilbao Spain Center for the Development of Nanoscience and Nanotechnology 9170124 Estación Central Chile Departamento de Física Universidad de Santiago de Chile (USACH) Avenida Víctor Jara 3493 9170124 Estación Central Chile

We propose a high-quality deterministic single-photon source which can not only works in the ultrastrong light-matter coupling regime, but also emits two single photons with an arbitrary time separation, in one excitation process. We find that the special solutions of the two-qubit quantum Rabi and Jaynes-Cummings models which have at most one photon and constant eigenenergies can be used to implement this proposal through two consecutive adiabatic evolutions, and the system goes back to the initial state of the next period automatically after photon emission. Due to their peculiarities and reach of the ultrastrong coupling, the adiabatic evolution can be quite fast and further accelerated with the Stark shift. This Letter paves the way to fast quantum information protocols which take advantage of the ultrastrong coupling and avoid its dynamical complexity simultaneously.

关键词： Cavity quantum electrodynamics Circuit quantum electrodynamics Quantum description of light-matter interaction Quantum simulation Single photon sources Jaynes-Cummings model Rabi model

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