Sewage network operational risks based on InfoWorks ICM with nodal flow diurnal patterns under NPIs for COVID-19.

Non-Pharmaceutical Interventions (NPIs) have been widely employed globally over the past three years to control the rapid spread of coronavirus disease 2019 (COVID-19). These measures have imposed restrictions on urban residents' activities and significantly influenced sewage discharge characteristics within sewage network, particularly in densely populated cities in China. This study focused on the nodal flow diurnal patterns and sewage network operational risks before and after epidemic lockdown in Beijing from March to May in 2022. Nodal flow diurnal patterns on weekdays and weekends before and after NPIs were analyzed using measured data through statistical and mathematical methods. A sewage network model was established to simulate and analyze the operational risks based on InfoWorks ICM before and after epidemic lockdown. The main conclusions were as follows: (1) In predominantly residential areas, the total wastewater volume increased by approximately 28.76 % to 33.52 % after the implementation of strict NPIs. The morning and midday "M" peaks on normalized weekdays transformed into "N" peaks, and the morning peak time was delayed by 0.5 to 1 hour after the lockdown; (2) Following NPIs, More than 90 % of manholes' average water levels rose to varying degrees, approximately 50 % of pipe lengths exhibited a full flow state; (3) When the lockdown was in place during a hot summer day, sewage overflow phenomena were observed in 4.6 % and 9.6 % of manholes, respectively, with per capita daily drainage equivalent reaching 40-50 %. These findings hold significant implications for the proactive planning and operational management of water industry infrastructure during major emergencies.

Long non-coding RNA CERS6-AS1 plays a prognostic role in promoting the progression of gastric cancer.

This study aims to investigate the potential clinical function of long non-coding RNA CERS6-AS1 (lncRNA CERS6-AS1) integrated miR-567 in gastric cancer. The expression of CERS6-AS1 in gastric cancer tissues was detected through RT-qPCR in contrast to the normal tissues. The correlation between the expression of lncRNA CERS6-AS1 and the characteristics of clinical data was analyzed. Kaplan-Meier curve was used to assess the survival analysis, while Cox proportional hazards model multivariate analysis was performed to evaluate the prognostic risk factors of gastric cancer to verify the prognostic possibility of CERS6-AS1. The expression of CERS6-AS1 in different gastric cancer cells was detected, being the development of gastric cancer cells after knockdown CERS6-AS1 studied using CCK-8, Transwell migration, and invasion detection methods. The targeting effect and interaction between CERS6-AS1 and miR-567 through biological analysis and luciferase activity detection. The expression of lncRNA CERS6-AS1 was elevated in gastric cancer tissues and cells. The results of this study demonstrate that the condition of gastric cancer patients was related to the expression of CERS6-AS1, and therefore CERS6-AS1 might be a prognostic factor for the progression of gastric cancer. In addition, the ability of gastric cancer cells to proliferate, migrate and invade could be reduced by knockdown CERS6-AS1. After CERS6-AS1 knockdown, the expression level of miR-567 in gastric cancer tissues decreased, while the expression level of miR-567 increased. In conclusion, lncRNA CERS6-AS1 might promote the progression of gastric cancer and had the potential as a prognostic marker of gastric cancer.

Attention-based deep residual learning network for entity relation extraction in Chinese EMRs.

BACKGROUND: Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of clinical texts, resulting in an improved quality of health-related data analysis. Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction. METHODS: This paper proposes an Attention-Based Deep Residual Network (ResNet) model to recognize medical concept relations in Chinese EMRs. RESULTS: Our model achieves F1-score of 77.80% on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches. CONCLUSION: The residual network-based model can reduce the negative impact of corpus noise to parameter learning, and the combination of character position attention mechanism will enhance the identification features of different type of entities.