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Hydrogen peroxide-independent age group of superoxide catalyzed simply by soy bean peroxidase in response to ferrous ion.
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the presented data is not sufficient. This model is first trained using a GISAID dataset with given virus types detected according to the molecular differences in coronavirus populations by phylogenetic tree clustering. In a second step, we apply the trained model to another but unlabeled SARS-CoV-2 virus dataset. For these data, we can either assign a virus type to the sequences or reject atypical samples. Those rejected sequences allow to speculate about new virus types with respect to nucleotide base mutations in the viral sequences. Moreover, this rejection analysis improves model robustness. Last but not least, the presented approach has lower computational complexity compared to methods based on (multiple) sequence alignment.
The online version contains supplementary material available at 10.1007/s00521-021-06018-2.
The online version contains supplementary material available at 10.1007/s00521-021-06018-2.We document a causal effect of the conservative Fox News Channel in the USA on physical distancing during COVID-19 pandemic. We measure county-level mobility covering all US states and District of Columbia produced by GPS pings to 15-17 million smartphones and zip-code-level mobility using Facebook location data. Using the historical position of Fox News Channel in the cable lineup as the source of exogenous variation, we show that increased exposure to Fox News led to a smaller reduction in distance traveled and a smaller increase in the probability of staying home after the national emergency declaration in the USA. Our results show that slanted media can have a harmful effect on containment efforts during a pandemic by affecting people's behavior.In this era of wireless COVID-19 telehealth, visiting hospital for regular follow-ups could invite coronavirus in someone's body. Opting for proactive E-health services is the best thing. It helps the remote patients to share their confidential data through secured encryption. Telehealth services are emerging element in these proactive medical sciences. It helps the remote patients to share their confidential data through secured transmission. In this paper, amino acid guided matrix encoding scheme has been proposed. White blood cell count or Leukocute count is a dominant indicator of patients' health condition, even amid COVID-19. An abnormal growth in leukocyte count is mainly caused due to an infection, cancer, or any other severe symptoms. It initiates internal haematological inflammations, cardiovascular diseases, Type II diabetes, etc. Therefore, tracking leukocyte count may for disease diagnosis and further treatments. The leukocyte count is generally done in different pathologies, and the data evaluation needs the expertise of a pathologist. In this paper, a technique involving security measures to transmit the result of the histological test with the help of cryptography has been proposed. The data to be transferred to the concerned physician for further diagnosis with the help of proposed way of encryption using amino acids, which ensures no data loss, no data modification, no data theft in the middle of transmission. The proposed encryption method using the amino acid codes has produced results showing satisfactory performances such as p-values found to be 7.215544e-04 and 8.48904e-03 for the key stream and cipher key matrix monobit test respectively, and 8.10245e-04 and 8.10245e-04 for the key stream and cipher key matrix frequency test respectively. It may be used as a transmission module in any wireless COVID-19 Telehealth Systems.The stochastic elasticity of variance model introduced by Kim et al. selleck compound (Appl Stoch Models Bus Ind 30(6)753-765, 2014) is a useful model for forecasting extraordinary volatility behavior which would take place in a financial crisis and high volatility of a market could be linked to default risk of option contracts. So, it is natural to study the pricing of options with default risk under the stochastic elasticity of variance. Based on a framework with two separate scales that could minimize the number of necessary parameters for calibration but reflect the essential characteristics of the underlying asset and the firm value of the option writer, we obtain a closed form approximation formula for the option price via double Mellin transform with singular perturbation. Our formula is explicitly expressed as the Black-Scholes formula plus correction terms. The correction terms are given by the simple derivatives of the Black-Scholes solution so that the model calibration can be done very fast and effectively.
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