In December 2019, an outbreak of an unknown cause of pneumonia (later named coronavirus disease 2019 [COVID-19]) occurred in Wuhan, China. This was found to be attributed to a novel coronavirus of zoonotic origin, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously named 2019 novel coronavirus or 2019-nCoV). The SARS-CoV-2, a new type of highly pathogenic human coronavirus related to severe acute respiratory syndrome coronavirus (SARS-CoV), spread rapidly worldwide and caused 246,303,023 confirmed infections, including 4,994,160 deaths, by October 31, 2021. SARS-CoV-2 and SARS-CoV vary in their specific characteristics, regarding epidemics and pathogenesis. This article focuses on the comparison of the virology, epidemiology, and clinical features of SARS-CoV and SARS-CoV-2 to reveal their common and distinct properties, to provide an up-to-date resource for the development of advanced systems and strategies to monitor and control future epidemics of highly pathogenic human coronaviruses.

Catheter-related blood-stream infections (CRBSIs) are the most common healthcare-associated blood-stream infections. They can be diagnosed by either semi-quantitative or quantitative methods, which may differ in diagnostic accuracy. A meta-analysis was undertaken to compare the diagnostic accuracy of semi-quantitative and quantitative methods for CRBSI. A systematic search of Medline, Scopus, Cochrane and Embase databases up to January 2020 was performed and subjected to a QUADAS (quality assessment of diagnostic accuracy studies 2) tool to evaluate the risk of bias among studies. The pooled sensitivity and specificity of the methods were determined and heterogeneity was evaluated using the χ2 test and I2. Publication bias was assessed using a Funnel plot and the Egger's test. In total, 45 studies were analysed with data from 11 232 patients. The pooled sensitivity and specificity of semi-quantitative methods were 85% (95% CI 79–90%) and 84% (95% CI 79–88%), respectively; and for quantitative methods were 85% (95% CI 79–90%) and 95% (95% CI 91–97%). Considerable heterogeneity was statistically evident (P < 0.001) by both methods with a correspondingly symmetrical Funnel plot that was confirmed by a non-significant Deek's test. We conclude that both semi-quantitative and quantitative methods are highly useful for screening for CRBSI in patients and display high sensitivity and specificity. Quantitative methods, particularly paired quantitative cultures, had the highest sensitivity and specificity and can be used to identify CRBSI cases with a high degree of certainty.

Dome A is the highest ice feature in Antarctica, at over 4000 m a.s.l., and overlies the Gamburtsev mountains. It is believed that deep ice-core drilling in this area may reveal the oldest ice in Antarctica, providing an unparalleled paleoclimate record in excess of 1 Ma, and perhaps going back as far as 1.2 Ma (Xiao and others, 2008). However, little is known about this area because its height and distance from the coast and scientific stations makes access difficult.

Blue-ice areas (BIAs) and their geographical distribution in Antarctica were mapped using Landsat-7 ETM+ images with 15 m spatial resolution obtained during the 1999–2003 austral summers and covering the area north of 82.5° S, and a snow grain-size image of the MODIS-based Mosaic of Antarctica (MOA) dataset with 125 m grid spacing acquired during the 2003/04 austral summer from 82.5°S to the South Pole. A map of BIAs was created with algorithms of thresholds based on band ratio and reflectance for ETM+ data and thresholds based on snow grain size for the MOA dataset. The underlying principle is that blue ice can be separated from snow or rock by their spectral discrepancies and by different grain sizes of snow and ice. We estimate the total area of BIAs in Antarctica during the data acquisition period is 234 549 km2, or 1.67% of the area of the continent. Blue ice is scattered widely over the continent but is generally located in coastal or mountainous regions. The BIA dataset presented in this study is the first map covering the entire Antarctic continent sourced solely from ETM+ and MODIS data. This dataset can potentially benefit other studies in glaciology, meteorology, climatology and paleoclimate, meteorite collection and airstrip site selection.

Two algorithms for constructing a class of compactly supported conjugate symmetric complex tight wavelet frames ψ = {ψ1, ψ2} are derived. Firstly, a necessary and sufficient condition for constructing the conjugate symmetric complex tight wavelet frames is established. Secondly, based on a given conjugate symmetric low pass filter, a description of a family of complex wavelet frame solutions is provided when the low pass filter is of even length. When one wavelet is conjugate symmetric and the other is conjugate antisymmetric, the two wavelet filters can be obtained by matching the roots of associated polynomials. Finally, two examples are given to illustrate how to use our method to construct conjugate symmetric complex tight wavelet frames which have some vanishing moments.