X-ray diffraction, Raman spectroscopy, and electrical conductivity measurements of molybdenum disulfide MoS(2) are performed at pressures up to 81 GPa in diamond anvil cells. Above 20 GPa, we find discontinuous changes in Raman spectra and x-ray diffraction patterns which provide evidence for isostructural phase transition from 2H(c) to 2H(a) modification through layer sliding previously predicted theoretically. This first-order transition, which is completed around 40 GPa, is characterized by a collapse in the c-lattice parameter and volume and also by changes in interlayer bonding. After the phase transition completion, MoS(2) becomes metallic. The reversibility of the phase transition is identified from all these techniques.

Background: The World Health Organization has declared coronavirus disease 2019 (COVID-19) a public health emergency of global concern. Updated analysis of cases might help identify the risk factors of illness severity. Results: The median age was 63 years, and 44.9% were severe cases. Severe patients had higher APACHE II (8.5 vs. 4.0) and SOFA (2 vs. 1) scores on admission. Among all univariable parameters, lymphocytes, CRP, and LDH were significantly independent risk factors of COVID-19 severity. LDH was positively related both with APACHE II and SOFA scores, as well as P/F ratio and CT scores. LDH (AUC = 0.878) also had a maximum specificity (96.9%), with the cutoff value of 344.5. In addition, LDH was positively correlated with CRP, AST, BNP and cTnI, while negatively correlated with lymphocytes and its subsets. Conclusions: This study showed that LDH could be identified as a powerful predictive factor for early recognition of lung injury and severe COVID-19 cases. Methods: We extracted data regarding 107 patients with confirmed COVID-19 from Renmin Hospital of Wuhan University. The degree of severity of COVID-19 patients (severe vs. non-severe) was defined at the time of admission according to American Thoracic Society guidelines for community acquired pneumonia.

Fact sheets with vivid graphical design and intriguing statistical insights are prevalent for presenting raw data. They help audiences understand data-related facts effectively and make a deep impression. However, designing a fact sheet requires both data and design expertise and is a laborious and time-consuming process. One needs to not only understand the data in depth but also produce intricate graphical representations. To assist in the design process, we present DataShot which, to the best of our knowledge, is the first automated system that creates fact sheets automatically from tabular data. First, we conduct a qualitative analysis of 245 infographic examples to explore general infographic design space at both the sheet and element levels. We identify common infographic structures, sheet layouts, fact types, and visualization styles during the study. Based on these findings, we propose a fact sheet generation pipeline, consisting of fact extraction, fact composition, and presentation synthesis, for the auto-generation workflow. To validate our system, we present use cases with three real-world datasets. We conduct an in-lab user study to understand the usage of our system. Our evaluation results show that DataShot can efficiently generate satisfactory fact sheets to support further customization and data presentation.

BACKGROUNDThe World Health Organization (WHO) has recently declared coronavirus disease 2019 (COVID-19) a public health emergency of global concern. Updated analysis of cases might help identify the characteristic and risk factors of the illness severity. METHODSWe extracted data regarding 47 patients with confirmed COVID-19 from Renmin Hospital of Wuhan University between February 1 and February 18, 2020. The degree of severity of COVID-19 patients (severe vs. non-severe) was defined at the time of admission according to American Thoracic Society (ATS) guidelines for communityacquired pneumonia (CAP). RESULTSThe median age was 64.91 years, 26 cases (55.31%) were male of which, and 70.83% were severe cases. Severe patients had higher APACHE II (9.92 vs 4.74) and SOFA (3.0 vs 1.0) scores on admission, as well as the higher PSI (86.13 vs 61.39), Curb-65 (1.14 vs 0.48) and CT semiquantitative scores (5.0 vs 2.0) when compared with nonsevere patients. Among all univariable parameters, APACHE II, SOFA, lymphocytes, CRP, LDH, AST, cTnI, BNP, et al were significantly independent risk factors of COVID-19 severity. Among which, LDH was most positively related both with APACHE II (R = 0.682) and SOFA (R = 0.790) scores, as well as PSI (R = 0.465) andCT (R = 0.837) scores. To assess the diagnostic value of these selected parameters, LDH (0.9727) had maximum sensitivity (100.00%) and specificity (86.67%), with the cutoff value of 283. As a protective factor, lymphocyte counts less than 1.045 x 10 9 /L showed a good accuracy for identification of severe patients with AUC = 0.9845 (95%CI 0.959-1.01), the maximum specificity (91.30%) and sensitivity (95.24%). In addition, LDH was positively correlated with CRP, AST, BNP and cTnI, while negatively correlated with lymphocyte cells and its subsets, including CD3 + , CD4 + and CD8 + T cells (P < 0.01).