COVID-19 and stock market nexus: evidence from Shanghai Stock Exchange

The outbreak of the contagious pandemic Covid-19 has disturbed various economic and business activities across the globe. This has resulted in the declination of cash flows and revenues, which significantly increases the probability of corporate bankruptcies and adversely affects the stock market performance. The current study investigated the Covid-19 and stock market nexus whilst considering the Shanghai Stock Exchange (SSX). Covid-19 active cases and deaths have been considered proxies for the Covid-19 from 1 April 2020 to 30 July 2020. For empirical analysis, this study utilised quantile regression and dynamic ordinary least squares (DOLS). Empirical findings of both quantile regression and DOLS illustrate that both the Covid-19 active cases and deaths significantly decline the SSX closing index. However, the quantile estimates reveal that from lower (0.25) quantile to medium (0.50) to higher (0.75) quantile, the magnitude of these variables is found declining. Moreover, the frequency domain causality test confirmed the unidirectional causal relationship between the study variables, running from Covid-19 cases and deaths to the SSX. The findings are robust, which leads to providing practical policy implications that identified the need to revise health-finance-related policies and financial education to combat such circumstances in the future

COVID-19 and currency market: a comparative analysis of exchange rate movement in China and USA during pandemic

By aiming to understand the importance of the ongoing effects of COVID-19 on several economies, this study attempts to highlight the impact of COVID-19 confirmed cases and deaths occurring in the most affected countries of the world such as China and the USA, especially in the context of their respective currencies. For this purpose, the daily data from January 22, 2020, till May 7, 2021, has been taken into account, and then has been analyzed to examine the linear relationship between the currency and proxies of the COVID-19 pandemic. Moreover, the (autoregressive distributed lag) ARDL model has been employed in order to fulfil the research objective with numerous statistical diagnostic measures, so as to validate the model. The results indicate that the exchange rate is affected negatively due to the effect of COVID-19 cases and deaths in particular countries. Moreover, the effect of the pandemic is not limited to the short run only, but also in the long run, as it is hurting the financial backbone of China and the USA

Single-shot quantitative differential phase contrast imaging combined with programmable polarization multiplexing illumination

We propose a single-shot quantitative differential phase contrast (DPC) method with polarization multiplexing illumination. In the illumination module of our system, the programmable LED array is divided into four quadrants and covered with polarizing films of four different polarization angles. We use a polarization camera with polarizers before the pixels in the imaging module. By matching the polarization angle between the polarizing films over the custom LED array and the polarizers in the camera, two sets of asymmetric illumination acquisition images can be calculated from a single-shot acquisition image. Combined with the phase transfer function, we can calculate the quantitative phase of the sample. We present the design, implementation, and experimental image data demonstrating the ability of our method to obtain quantitative phase images of the phase resolution target, as well as Hela cells.Comment: 5 pages,4figure

FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless Communication Networks

With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. However, when a federated learning system runs on wireless communication networks, limited wireless resources, heterogeneity of clients, and network transmission failures affect its performance and accuracy. In this study, we propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training accuracy and performance in wireless communication networks. We utilize a tiering algorithm that dynamically divides clients into different tiers according to specific indicators and assigns specific timeout thresholds to each tier to reduce the training time required. To improve the accuracy of the model without increasing the training time, we introduce a cross-tier client selection algorithm that can effectively select the tiers and participants. Simulation experiments show that our scheme can make the model converge faster and achieve a higher accuracy in wireless communication networks

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine

Serum and plasma contain abundant biological information that reflect the body’s physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases