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UVA photolysis of nitrite (NO2-) occurs in a number of natural and engineered aquatic systems. This study reports for the first time that pathogenic microorganisms can be effectively inactivated during the coexposure of UVA irradiation and NO2- under environmentally relevant conditions. The results demonstrated that more than 3 log inactivation of Escherichia coli K-12, Staphylococcus aureus, and Spingopyxis sp. BM1-1 was achieved by UVA photolysis of 2.0 mg-N L-1 of NO2- in synthetic drinking water and real surface water. The inactivation was mainly attributed to the reactive species generated from UVA photolysis of NO2- rather than UVA irradiation or NO2- oxidation alone. The inactivation was predominantly contributed by the reactive nitrogen species (NO2 center dot and ONOO-/HOONO) instead of the reactive oxygen species (HO(center dot )or O-2(center dot-)). A kinetic model to simulate the reactive species generation from UVA photolysis of NO2- was established, validated, and used to predict the contributions of different reactive species to the inactivation under various environmental conditions. Several advanced tools (e.g., D2O - labeling with Raman spectroscopy) were used to demonstrate that the inactivation by the UVA/NO2- treatment was attributed to the DNA destruction by the reactive nitrogen species, which completely suppressed the viable but nonculturable (VBNC) states and the reactivation of bacteria. This study highlights a novel process for the inactivation of pathogenic microorganisms in water and emphasizes the critical role of reactive nitrogen species in water disinfection and purification.

Sulfate radical (SO4˙−)-based advanced oxidation processes (AOPs) have emerged as promising technologies for inactivation of pathogenic microorganisms in water and wastewater, due to the high oxidation potential and selectivity of SO4˙− towards different microorganisms, and the negligible formation of undesired byproducts by using these processes. This book chapter provides an overview of the kinetic responses of various pathogenic/indicator microorganisms toward different SO4˙− based AOPs (e.g., metal-assisted, light-assisted, and piezo-catalytic ones) and the mechanisms responsible for the inactivation (e.g., via membrane destruction, enzyme and genome damage, and affecting repair that doesn't need light). The effects of solution chemistry (e.g., pH, temperature, and various water matrices) on the disinfection efficacies have also been reviewed and discussed. The formation of undesired byproducts in the SO4˙− based AOPs is also reviewed and discussed. Key points from the review are summarized in the conclusive remarks. Knowledge gaps, research needs, and design requirements for the engineering applications of these processes in real-world practice are proposed as future perspectives.

© 2020, The Author(s). Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.

Weiqi Liu,1,* Weiling Liu,2,* Shaoling Wang,3 Huichun Tong,4 Jianmin Yuan,5 Zhenning Zou,6 Jianwen Liu,7 Donghai Yang,8 Zhongxing Xie9 1Department of Clinical Laboratory, Maternal and Children Health Care Hospital (Huzhong Hospital) of Huadu, Guangzhou, Guangdong, 510800, People’s Republic of China; 2Department of Clinical Laboratory, Chancheng Centre Hospital, Foshan, Guangdong, 528000, People’s Republic of China; 3Department of Clinical Laboratory, Taishan People’s Hospital, Jiangmen, Guangdong, 529200, People’s Republic of China; 4Department of Clinical Laboratory, Boai Hospital of Zhongshan, Zhongshan, Guangdong, 528402, People’s Republic of China; 5Department of Clinical Laboratory, Humen Hospital, Dongguan, Guangdong, 523899, People’s Republic of China; 6Department of Clinical Laboratory, Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, Guangdong, 518028, People’s Republic of China; 7Department of Clinical Laboratory, Huiyang Sanhe Hospital, Huizhou, Guangdong, 516211, People’s Republic of China; 8Department of Clinical Laboratory, Sanzhao Town Health Center, Jinwan District, Zhuhai, Guangdong, 519040, People’s Republic of China; 9Department of Clinical Laboratory, The Second People’s Hospital of Zhaoqing, Zhaoqing, Guangdong, 526060, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shaoling Wang Email shaoling_w@hotmail.comBackground: In China, the prevalence of HUA in the Pearl River Delta (PRD) region of Guangdong Province has not been extensively investigated. Therefore, this study investigated the prevalence of HUA and its related factors among people aged 20– 99 years in nine cities in the PRD.Materials and Methods: We selected 6491 health check participants from 9 cities in the PRD and collected participants’ anthropometric and biochemical test results for a cross-secti