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BackgroundPrevious studies suggest that coronavirus disease 2019 (COVID-19) is a systemic infection involving multiple systems, and may cause autonomic dysfunction.ObjectiveTo assess autonomic function and relate the findings to the severity and outcomes in COVID-19 patients.MethodsWe included consecutive patients with COVID-19 admitted to the 21st COVID-19 Department of the east campus of Renmin Hospital of Wuhan University from February 6 to March 7, 2020. Clinical data were collected. Heart rate variability (HRV), N-terminal pro-B-type natriuretic peptide (NT-proBNP), D-dimer, and lymphocytes and subsets counts were analysed at two time points: nucleic-acid test positive and negative. Psychological symptoms were assessed after discharge.ResultsAll patients were divided into a mild group (13) and a severe group (21). The latter was further divided into two categories according to the trend of HRV. Severe patients had a significantly lower standard deviation of the RR intervals (SDNN) (P < 0.001), standard deviation of the averages of NN intervals (SDANN) (P < 0.001), and a higher ratio of low- to high-frequency power (LF/HF) (P = 0.016). Linear correlations were shown among SDNN, SDANN, LF/HF, and laboratory indices (P < 0.05). Immune function, D-dimer, and NT-proBNP showed a consistent trend with HRV in severe patients (P < 0.05), and severe patients without improved HRV parameters needed a longer time to clear the virus and recover (P < 0.05).ConclusionHRV was associated with the severity of COVID-19. The changing trend of HRV was related to the prognosis, indicating that HRV measurements can be used as a non-invasive predictor for clinical outcome.

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.