ABSTRACT
PurposeThis paper aims to examine the hedge, diversifier and safe haven properties of the global listed infrastructure sector and subsector indices against two traditional asset classes, stocks and bonds, and four alternative asset classes, including commodities, real estate, private equity and hedge funds during extreme negative stock market movements.Design/methodology/approachUsing dynamic conditional correlation and quantile regression, the authors analyze a data set of 12 indices comprising listed infrastructure and traditional asset classes from 2010 to 2019.FindingsOverall, the findings indicate that listed infrastructure acts as an effective diversifier but not as a strong safe haven or hedge when considered in a multiasset context. With minor exceptions, listed infrastructure cannot be concluded as a safe haven against other asset classes under investigation.Practical implicationsThe present study has implications for institutional investors looking to incorporate infrastructure in their multiasset portfolios for increased portfolio diversification benefits.Originality/valueDespite the increased influence of infrastructure as an asset class, to the best of the authors' knowledge, this is the first study to investigate the hedge, safe haven and diversifying properties of infrastructure in a multi-asset context.
ABSTRACT
Coronavirus illness (COVID-19), discovered in late 2019, has spread rapidly worldwide, resulting in significant mortality. This study analyzed the performance of studies that employed machines and DL on chest X-ray pictures and CT scans for COVID-19 diagnosis. ML approaches on CT and X-ray images aided incorrectly in identifying COVID-19. The fast spread of COVID-19 worldwide and the growing number of deaths necessitates an immediate response from all sectors. Authorities will be able to deal with the effects more efficiently if such illnesses can be predicted in the future. Furthermore, it is crucial to maintain track of the number of infected persons through regular check-ups, and it is frequently required to confine affected people and implement medical treatments. In addition, various additional elements, such as environmental influences and commonalities among the most afflicted places, should be considered to slow the spread of COVID-19, and precautions should be taken. AI-based approaches for the prediction and diagnosis of COVID-19 were suggested in this paper. This Review Article discusses current advances in AI technology and its biological applications, particularly the coronavirus.
ABSTRACT
The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.
ABSTRACT
Nucleocapsid protein (N protein) is the primary antigen of the virus for development of sensitive diagnostic assays of COVID-19. In this paper, we demonstrate the significant impact of dimerization of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) N-protein on sensitivity of enzyme-linked immunosorbent assay (ELISA) based diagnostics. The expressed purified protein from E. coli is composed of dimeric and monomeric forms, which have been further characterized using biophysical and immunological techniques. Indirect ELISA indicated elevated susceptibility of the dimeric form of the nucleocapsid protein for identification of protein-specific monoclonal antibody as compared to the monomeric form. This finding also confirmed with the modelled structure of monomeric and dimeric nucleocapsid protein via HHPred software and its solvent accessible surface area, which indicates higher stability and antigenicity of the dimeric type as compared to the monomeric form. The sensitivity and specificity of the ELISA at 95% CI are 99.0% (94.5-99.9) and 95.0% (83.0-99.4), respectively, for the highest purified dimeric form of the N protein. As a result, using the highest purified dimeric form will improve the sensitivity of the current nucleocapsid-dependent ELISA for COVID-19 diagnosis, and manufacturers should monitor and maintain the monomer-dimer composition for accurate and robust diagnostics.