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Background: We aimed to assess the psychological burden in healthcare professionals during the COVID-19 outbreak, particularly in locked-down Chinese cities, to provide evidence for exploring early preventive interventions. Methods: A cross-sectional study was designed, with an online questionnaire for data collection on the psychological distress, stress, and anxiety subscales of the Depression Anxiety Stress Scale 21, and on social and environmental factors, particularly the influence of city lockdown, among healthcare professionals exposed to COVID-19, February 4–16, 2020. Findings: Data were collected from 636 medical professionals and 1929 college students. Psychological burden includes psychological distress (unpleasant feelings or emotions that impact normal activities of daily living), stress (physical, mental, or emotional factor that causes bodily or mental tension), and anxiety (a feeling of apprehension and fear, characterized by physical symptoms such as palpitations, sweating, and feelings of stress), accounting for 36·48%, 13·36%, and 25·31%, respectively, among medical professionals. Medical staff with COVID-19 exposure were 2·04 times more likely to be anxious than non-exposed staffs (95% CI 1·14–3·63). The psychological distress of medical staff increased directly with the shortage of medical supplies (AOR=6·70; 95% CI 2·37–18·92). City lockdown was a critical factor that caused psychological burden. Interpretation: Healthcare professionals, especially those with exposure to COVID-19 in locked-down cities, were in a poor psychological state during the COVID-19 outbreak. Intervention and prevention programs that aim to address the psychological burden of healthcare professionals facing emerging infectious diseases are urgently needed. Funding Statement: This study was funded by the Shantou Science and Technology Project [2020] (number 6-5). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The research content and process have been approved by the Institutional Review Board of Shantou University Medical College (Project No. SUMC2020-01). All interviewees provided written informed consent.

We propose an online adaptive computational ghost imaging (CGI) scheme that offers high quality imaging with few measurement numbers. Different from the conventional second-order correlation CGI, the proposed scheme utilizes an online imaging method for image reconstruction, where the object image is recovered immediately after the light intensity is acquired. An adaptive measurement termination method is proposed to terminate the online imaging when the quality of the recovered image is satisfied. The effectiveness of the proposed scheme is verified by numerical simulations and experiments and its imaging performance is assessed by comparing the results with state-of-the-art FWHTGI (fast Walsh-Hadamard transform CGI) and RDCGI (Russian dolls ordering CGI). The results demonstrate that the proposed scheme can achieve high-quality imaging with few measurements, and imaging quality is better than that with FWHTGI and RDCGI under low measurement number conditions. The proposed scheme may be helpful for high-resolution and real-time CGI.