A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches.

The Eye of the Storm: COVID-19 Vaccination and the Eye.

The COVID-19 pandemic has galvanized the global response towards the development of new vaccines based on novel technologies at an unprecedented pace. Since the widespread implementation of vaccination campaigns, case reports on vaccines' systemic side effects, including ocular manifestations, have emerged. Since administered vaccines are generally not able to cause the disease in the recipient, or induce an immune response against the pathogen, we hypothesize that the development of ocular phenomena post-COVID-19 vaccination may occur via an immune response elicited by the vaccine. Of many, the most common ocular adverse events include facial nerve palsy, central venous sinus thrombosis and acute anterior uveitis. These COVID-19 vaccine-induced ocular (CVIO) adverse events could resemble the ocular findings in some of the COVID-19 patients. This review will provide a comprehensive overview of published ocular side effects potentially associated with COVID-19 vaccination and serve as a springboard for further research into CVIO adverse events.

COVID 19 pandemic;a review on understanding the mode of transmission and application of strategies to delay/stop transmission with special reference to India. (Special Issue: COVID-19 one health approach.)

Background: SARS CoV-2 has globally affected more than 31 hundred thousand population with more than 2 hundred thousand deaths till date, since its inception from Wuhan, China in December 2019. World is still clueless regarding definitive treatment for this highly contagious disease. Till then we have to rely on time tested methods to delay/stop transmission of this viral disease by observing social isolation, hand hygiene and wearing face mask.

Restarting LDLT During COVID-19: Early Results After Restructuring.

INTRODUCTION: Living-donor liver transplantation (LDLT) has been mostly suspended and deceased-donor living transplantation activity has been considerably reduced because of coronavirus disease 2019 (COVID-19). We modified our protocols and procedures in line with COVID-19 guidelines. Since the restructuring, we have performed 20 LDLTs. Our study reports the outcomes of these cases and demonstrates the feasibility of LDLT during this pandemic. MATERIALS AND METHODS: The changes were influenced by experiences and communications from across the globe. A month-long self-imposed moratorium was spent in restructuring the program and implementing new protocols. Twenty LDLTs were performed between April 18 and September 15 using the new protocols. Our experience includes 2 simultaneous liver-kidney transplants, 1 ABO-incompatible LDLT, and 1 pediatric case (age 11 months). RESULTS: Nineteen patients recovered and 1 patient died. We maintained our postoperative immunosuppression protocol without many changes. Major complications were observed in 30% of recipients but none of the donors. One recipient was infected with COVID-19 during the postoperative period. A donor-recipient couple contracted COVID-19 after discharge from the hospital. All patients recovered from COVID-19 and liver enzymes were unaffected. CONCLUSION: This study represents a microcosm of experience in LDLT during the COVID-19 era. Outcomes of LDLT are not affected by COVID-19 per se, provided that we make necessary changes.

Convalescent plasma therapy and remdesivir duo successfully salvaged an early liver transplant recipient with severe COVID-19 pneumonia.

The impact and clinical spectrum of COVID-19 infection in liver transplant recipients/solid organ transplants are being unveiled during this recent pandemic. The clinical experience of use of current antiviral drugs and immunomodulators are sparse in solid organ transplantation. We present the clinical course of a 49-year-old male recipient who underwent living donor liver transplant for recurrent gastrointestinal bleed and contracted severe COVID-19 pneumonia during the third postoperative week. Herein we report the successful management of severe COVID-19 pneumonia using convalescent plasma therapy and remdesivir. Recipient's clinical deterioration was halted after three consecutive convalescent plasma transfusions with improvement in hypoxia and inflammatory markers (interleukin-6 and C-reactive protein). The use of convalescent plasma therapy along with remdesivir may be an ideal combination in the management of severe COVID-19 pneumonia in solid organ transplant recipients.

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.