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Background: The pandemic of novel coronavirus disease 2019 (COVID-19) has become a serious public health crisis worldwide. The symptoms of COVID-19 vary from mild to severe among different age groups, but the physiological changes related to COVID-19 are barely understood. Methods: In this study, a high-resolution mass spectrometry (HRMS)-based lipidomic strategy was used to characterize the endogenous plasma lipids for cured COVID-19 patients with different ages and symptoms. These patients were further divided into two groups: those with severe symptoms or who were elderly and relatively young patients with mild symptoms. In addition, automated lipidomic identification and alignment was conducted by LipidSearch software. Multivariate and univariate analyses were used for differential comparison. Results: Nearly 500 lipid compounds were identified in each cured COVID-19 group through LipidSearch software. At the level of lipid subclasses, patients with severe symptoms or elderly patients displayed dramatic changes in plasma lipidomic alterations, such as increased triglycerides and decreased cholesteryl esters. Some of these differential lipids might also have essential biological functions. Furthermore, the differential analysis of plasma lipids among groups was performed to provide potential prognostic indicators, and the change in signaling pathways. Conclusions: Dyslipidemia was observed in cured COVID-19 patients due to the viral infection and medical treatment, and the discharged patients should continue to do consolidation therapy. This work provides valuable knowledge about plasma lipid markers and potential therapeutic targets of COVID-19 and essential resources for further research on the pathogenesis of COVID-19.

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Conference proceedings are one of the key communication channels in computer science. This paper aims to analyze the Chinese outputs in the context of conference papers in computer science through an exploration of the conference proceedings series book -Lecture Notes in Computer Science (LNCS) in the period of 1997-2005. Results indicate that: 1. The number of Chinese papers in LNCS keeps growing in the studied period; the share of Chinese papers in LNCS in recent years is much higher than that of Chinese SCI papers in the world; In sharp contrast with remarkable growth of the share of Chinese papers in LNCS, the share of SCI articles in top journals of computer science published by the scientists of mainland China is negligible during the same period. 2. Chinese researchers are more likely to collaborate with domestic fellows; 3. In spite of the increasing amounts of Chinese papers in LNCS, they receive only a few citations; 4. The articles are strikingly more cited by authors themselves and international authors' citations are more than Chinese authors' non-self-citations in the first three years after publication; 5. Based on the new indicator Impact Index (II) the authors proposed, the relative impact of Chinese articles in LNCS is increasing although the average impact of Chinese papers in LNCS is obviously less than that of the publications in LNCS in each year during the studied period.

based upon the Science Citation Index (SCI) and China Scientific and Technical Papers and Citations (CSTPC) databases. We compare the international and domestic outputs of the key projects by applying various scientometric indicators and techniques. The findings indicate that, as a whole, the research performances of the key projects have, to different degrees, increased in both international and domestic papers during the period of study. Semiconductor is the internationally most productive sub-discipline and Automatization is the domestically most productive subdiscipline, measured on average per project. The Combination Impact Factor (CIF), which integrates the CSTPC-IF and the SCI-IF into the evaluation process, is further proposed for the combined evaluation of domestic and international outputs of the key projects. In terms of ratio of CIF relative to the funds in each sub-discipline, results also show that Semiconductor is the most productive sub-discipline and Computer is the least productive one. Using correlation analysis a significant and positive relationship between the SCI-IF and the CIF has been found for the evaluated projects.