IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion.

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 ± 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu “Qianxi” mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications.

Untangling the contributions of meteorological conditions and human mobility to tropospheric NO2 in Chinese mainland during the COVID-19 pandemic in early 2020.

In early 2020, unprecedented lockdowns and travel bans were implemented in Chinese mainland to fight COVID-19, which led to a large reduction in anthropogenic emissions. This provided a unique opportunity to isolate the effects from emission and meteorology on tropospheric nitrogen dioxide (NO2). Comparing the atmospheric NO2 in 2020 with that in 2017, we found the changes of emission have led to a 49.3 ± 23.5% reduction, which was ∼12% more than satellite-observed reduction of 37.8 ± 16.3%. The discrepancy was mainly a result of changes of meteorology, which have contributed to an 8.1 ± 14.2% increase of NO2. We also revealed that the emission-induced reduction of NO2 has significantly negative correlations to human mobility, particularly that inside the city. The intra-city migration index derived from Baidu Location-Based-Service can explain 40.4% ± 17.7% variance of the emission-induced reduction of NO2 in 29 megacities, each of which has a population of over 8 million in Chinese mainland.

Repositive RT-PCR test in discharged COVID-19 patients during medical isolation observation.

Objectives: The epidemiological and clinical characteristics of patients with coronavirus disease 2019 (COVID-19) have been researched. However, the prevalence of repositivity by real-time PCR for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear. Methods: A retrospective study was conducted involving 599 discharged patients with COVID-19 in a single medical centre. The clinical features of patients during their hospitalization and 14-day post-discharge quarantine were collected. Results: A total of 122 patients (20.4%) out of 599 patients retested positive after discharge. Specifically, 94 (15.7%) retested positive within 24 h of discharge, and another 28 patients (4.7%) were repositive on day 7 after discharge, although none showed any clinical symptomatic recurrence. Both repositives and non­repositives have similar patterns of IgG and IgM. Notably, the length of hospitalization of non-repositive patients was longer than that of 24-h repositive patients and 7-day repositive patients. In addition, the length of hospitalization of 24-h repositive patients was shorter than that of 7-day repositive patients, indicating that the length of hospitalization was also a determinant of viral shedding. Conclusion: Our study provides further information for improving the management of recovered and discharged patients, and further studies should be performed to elucidate the infectiveness of individuals with prolonged or RNA repositivity.