Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and viral pneumonia cases. The CXR images are divided into sub-blocks for finding out the discrete cosine transform (DCT) for every sub-block in this proposed method. In order to produce a compressed version for each CXR image, the DCT energy compaction capability is used. For each image, hardly few spectral DCT components are included as features. The dimension of the final feature vectors is reduced by scanning the compressed images using average pooling windows. In the 3-set classification, a multilayer artificial neural network is used. It is essential to triage non-COVID-19 patients with pneumonia to give out hospital resources efficiently. Higher size feature vectors are used for designing binary classification for COVID-19 and pneumonia. The proposed method achieved an average accuracy of 95% and 94% for the 3-set classification and binary classification, respectively. The proposed method achieves better accuracy than that of the recent state-of-the-art techniques. Also, the time required for the implementation is less.

Covid-19 Detection Based on Chest X-Ray Images Using DCT Compression and NN

Covid-19 is a highly contagious virus spreading all over the world. It is caused by SARS-CoV-2. virus. Some of the most common symptoms are fever, cough, sore throat, tiredness, and loss of smell or taste. There are two types of tests for COVID-19: the PCR test and the antigen test. Automatic detection of Covid-19 from publicly available resources is essential. This paper employs the commonly available chest x-ray (CXR) images in the classification of Covid-19, normal and viral pneumonia cases. The proposed method divides the CXR images into subblocks and computes the Discrete Cosine Transform (DCT) for every subblock. The DCT energy compaction capability is employed to produce a compressed version for each CXR image. Few spectral DCT components are incorporated as features for each image. The compressed images are scanned by average pooling windows to reduce the dimension of the final feature vectors. A multilayer artificial neural network is employed in the 3-set classification. The proposed method achieved an average accuracy of 95 %. While the proposed method achieves comparable accuracy relative to recent state-of-the-art techniques, its computational burden and implementation time is much less. © 2022 IEEE.

Strong anti-SARS-CoV-2 activity of Lucilia cuprina maggots’ excretion/secretion and its effect on viral entry and notch pathway in vitro: First work

The global pandemic caused by SARS-CoV-2 requires new lines of treatment to hinder viral entry and pathogenesis. Lucilia cuprina maggots’ excretion/secretion (E/S) contains proteases and antioxidants, among other active ingredients that contribute to its antibacterial, antifungal, and antiviral activity. This study aims to assess the potential effects of E/S on the entry and molecular pathogenesis of a SARS-CoV-2 isolate “NRC-03-nhCoV” in vitro for the first time. E/S was obtained from the collected maggots of L. cuprina that were maintained under controlled laboratory conditions. The E/S was used to treat VERO-E6 cells infected with SARS-CoV-2. The predicted antiviral activity of the E/S and the expression of the Notch pathway and viral pathogenesis-related genes were assessed at three time points. E/S showed potential antiviral activity against SARS-CoV-2 (IC50 = 0.324 µg/ml) with a high selectivity index value (SI = 572.997). Serine protease present in E/S was predicted to interact with transmembrane protease, serine 2 and cathepsin B. E/S was able to significantly downregulate Notch-related genes, SUMO1, and TDG in SARS-CoV-2-infected cells, shifting their expression toward levels of the control. Therefore, E/S of L. cuprina maggots is a potential strong inhibitor for SARS-CoV-2. © 2022. Mohammad R. K. Abdel-Samad et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Dividend policy, its asymmetric behavior and stock liquidity

Purpose: This paper examines the impact of dividend policy on stock market liquidity, and whether the dividend payouts has an asymmetric effect on stock liquidity. Design/methodology/approach: A multivariate panel-data regression analysis is conducted for a sample of the largest 411 nonfinancial US firms. Three main hypothesis are tested: (1) whether dividend payouts impact affect stock liquidity, (2) whether low and high dividend payments can asymmetrically effect on stock liquidity and (3) whether the presence of the GFC has an impact the relationship between dividend payments and stock liquidity. Findings: The study finds that dividend policy is adversely associated with stock liquidity. This supports the prediction of the liquidity-dividend hypothesis. The authors also report that stock liquidity asymmetrically responds to changes in dividend payouts, confirming the prediction of the dividend-signaling approach. More specifically, higher dividend payments decrease stock liquidity by a lower magnitude than the increase in stock liquidity resulting from lower dividend payments. Finally, the presence of the GFC weakened the relationship between dividend payments and stock liquidity. Research limitations/implications: The paper can help in performing future research by using different dataset covering the COVID-19 crisis. Practical implications: The paper allows market participants to better understand the impact of dividend policy and its asymmetric effects on stock liquidity. The authors’ analyses can direct investors and regulators to adopt new supervisory devices to create an appropriate level of dividend payouts that helps to effectively support the level of stock liquidity. Social implications: The paper intends to support the business community and to make strong contributions to the economic development and the welfare of the community. Originality/value: The originality comes from its new evidence as it can help in assessing the importance of dividend policy and its asymmetric impact on stock liquidity in the full sample and during the GFC. The paper is helpful in performing future analyses using a new sample period for another set of data as well as accounting for COVID-19 pandemic crisis. © 2022, Emerald Publishing Limited.