An Automatic Approach to Extracting Requirement Dependencies based on Semantic Web

Guan, Hui; Cai, Guorong; Zhao, Chuan

doi:10.1109/dsa52907.2021.00062

Cited by 3 publications

(1 citation statement)

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“…The research presented in this paper is a step toward advancing the field machine learning (Yin et al, 2024;Guan et al, 2024). While our contributions focus on the theoretical and technical advancements in machine learning, we acknowledge the broader societal implications as our work indirectly supports the development of technologies in delivery, transportation, and automated systems.…”

Section: Impact Statementmentioning

confidence: 98%

COVID-19 severity is associated with immunopathology and multi-organ damage

Chen

Zheng

et al. 2020

Preprint

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COVID-19 is characterised by dysregulated immune responses, metabolic dysfunction and adverse effects on the function of multiple organs. To understand how host responses contribute to COVID-19 pathophysiology, we used a multi-omics approach to identify molecular markers in peripheral blood and plasma samples that distinguish COVID-19 patients experiencing a range of disease severities. A large number of expressed genes, proteins, metabolites and extracellular RNAs (exRNAs) were identified that exhibited strong associations with various clinical parameters. Multiple sets of tissue-specific proteins and exRNAs varied significantly in both mild and severe patients, indicative of multi-organ damage. The continuous activation of IFN-I signalling and neutrophils, as well as a high level of inflammatory cytokines, were observed in severe disease patients. In contrast, COVID-19 in mild patients was characterised by robust T cell responses. Finally, we show that some of expressed genes, proteins and exRNAs can be used as biomarkers to predict the clinical outcomes of SARS-CoV-2 infection. These data refine our understanding of the pathophysiology and clinical progress of COVID-19 and will help guide future studies in this area.

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Section: Impact Statementmentioning

confidence: 98%