Have cryptocurrencies become an inflation hedge after the reopening of the U.S. economy?

In this paper, we study how the comovement between cryptocurrencies and the U.S. inflation expectation rates has changed during the post-reopening of the U.S. economy after the Covid-19 crisis. To do so, we develop a new concept of "exceedance co-kurtosis” which allows us to quantify asymmetry in strong comovement between each cryptocurrency and the inflation expectation rate. The key findings are as follows. First, we show the change in the co-kurtosis asymmetry for major cryptocurrencies: the downside co-kurtosis was higher than the upside co-kurtosis but it decreased after the reopening of the economy. Although the unconditional correlations between cryptocurrencies and the inflation expectation rates remain very low, our results indicate that the major cryptocurrencies become a slightly better inflation hedge after the reopening. Second and more interestingly, the results do not depend on whether a cryptocurrency has a cap on maximum supply or not. Therefore, treating the major cryptocurrencies as digital commodities could be misleading from the viewpoint of portfolio optimization. © 2023

Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams

Given a large, semi-infinite collection of co-evolving epidemiological data containing the daily counts of cases/deaths/recovered in multiple locations, how can we incrementally monitor current dynamical patterns and forecast future behavior? The world faces the rapid spread of infectious diseases such as SARS-CoV-2 (COVID-19), where a crucial goal is to predict potential future outbreaks and pandemics, as quickly as possible, using available data collected throughout the world. In this paper, we propose a new streaming algorithm, EPICAST, which is able to model, understand and forecast dynamical patterns in large co-evolving epidemiological data streams. Our proposed method is designed as a dynamic and flexible system, and is based on a unified non-linear differential equation. Our method has the following properties: (a) Effective: it operates on large co-evolving epidemiological data streams, and captures important world-wide trends, as well as location-specific patterns. It also performs real-time and long-term forecasting;(b) Adaptive: it incrementally monitors current dynamical patterns, and also identifies any abrupt changes in streams;(c) Scalable: our algorithm does not depend on data size, and thus is applicable to very large data streams. In extensive experiments on real datasets, we demonstrate that EPICAST outperforms the best existing state-of-the-art methods as regards accuracy and execution speed. © 2022 ACM.

Impact of The COVID-19 Pandemic on Neurology: Report from A General Hospital in Tokyo

Objective: The aim of this study was to investigate the impact of the coronavirus disease 2019 (COVID-19) pandemic on neurology in Japan, by analyzing data on the number of neurological patients at our hospital in Tokyo. Material and Methods: We counted the number of inpatients and outpatients per month;from January 2018 to September 2020. We defined the data from April 2020 to May 2020, as the first wave of the COVID-19 pandemic, and that from July 2020 to September 2020, as the second wave. The data from each wave were compared to those in the same period within the previous 2 years. We also analyzed other data;including, inpatients with stroke, outpatients with Parkinson’s disease, and outpatients with epilepsy. Results: In the first wave, the overall number of inpatients and outpatients greatly decreased;however, the number of inpatients with stroke increased. The ratio of outpatients with Parkinson’s disease, or outpatients with epilepsy to total outpatients also increased. In the second wave, the overall number of inpatients markedly increased, while that of outpatients slightly decreased. Conclusion: All Japanese general hospitals were greatly affected by the COVID-19 pandemic;especially in the first wave, even if the hospitals did not have in-hospital COVID-19 infection, or were not designated for COVID-19. Three factors;i.e. governmental, hospital, and patient factors, could affect the number of neurological patients during the COVID-19 pandemic. The numerical data reflecting the patients’ behavior might provide suggestions for addressing issues during other pandemics in the future. © 2021 JHSMR. Hosting by Prince of Songkla University. All rights reserved.

Severe COVID-19 Is an Acquired Red Cell Disease- Potential Mechanisms Underlying Endothelialitis

Rationale As the SARS-CoV-2 pandemic continues, there is an imperative to understand the pathophysiology underlying the associated critical illness. Evidence exists that COVID-19 is a systemic vasculopathy associated with a hypercoaguable state and recent meta analyses have shown that standard inflammatory biomarkers in critically ill COVID-19 patients are not significantly elevated when compared with similarly ill patients with non-SARS-CoV-2- related sepsis. Leveraging our novel microvasculature-on-a-chip microfluidics platform which permits tight biomechanical control, we investigate how physical interactions between the endothelium and COVID-19 patient red blood cells (RBCs) may directly cause endothelialitis. Methods In a single ICU, we enrolled patients with sepsis (SOFA score ≥3), and categorized as either resultant from COVID-19 (n=14) or alternative infection (n=15), collecting clinical data and whole blood. These samples were perfused through our systems comprising microchannels of 2 distinct diameters (40μm and 60μm) to approximate the size of postcapillary venules at physiologic shear rates (200 s-1 to 1000 s-1). Mean and peak velocity measurements as well as red cell aggregation were recorded using cell tracking with high resolution video microscopy. Plasma was prepared from these collections for measurement of syndecan-1 via ELISA to examine endothelial disturbance. Results Comparing demographic and clinical data, no significant differences were measured in co-morbidities, SOFA score or 30 day mortality between the two patient populations whereas there was a higher incidence of vasopressor use in the non-COVID sepsis group. There was no difference in mean or maximum velocity, however, blood from COVID-19 sepsis patients demonstrated increased red cell aggregation under dynamic conditions at lower shear rates (200 s-1- 400 s-1) when compared with patients with sepsis from non-COVID-19 causes (Figure, right panel). This effect was more pronounced in the smaller channel size. Additionally, syndecan-1 levels in patients with COVID-19 were elevated compared with patients with non-COVID-19 related sepsis, (812±357 ng/mL vs 469 ±193, p=0.15, Figure, left panel). Conclusions Our data support the hypothesis that COVID-19 results in an acquired RBC membrane pathology leading to aggregation. To our knowledge, this is the first study that proposes such a mechanism. We theorize this phenomenon directly disrupts the endothelial glycocalyx as the aggregates "rub" against it. Further work to isolate effects of red cell aggregation, cytokines and proteinases in plasma on endothelium and to demonstrate hypercoagulability in our microvasculature-on-chip system are underway. We believe our work suggests that therapies specifically targeting RBC aggregation may be beneficial for COVID-19 patients.