Effects of High Temperature on COVID‐19 Deaths in U.S. Counties

Abstract The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record‐hot or top‐10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID‐19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID‐19 cases and deaths. However, the associations between temperature and COVID‐19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID‐19 deaths during the summer of 2021 to examine the relationship between COVID‐19 deaths and temperature by applying a two‐stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID‐19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID‐19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID‐19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from −0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID‐19 deaths, which might help us to understand the underlying modulation of the COVID‐19 pandemic by meteorological variables and to develop epidemic policy response strategies

End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI.

Temporal correlation has been exploited for accelerated dynamic MRI reconstruction. Some methods have modeled inter-frame motion into the reconstruction process to produce temporally aligned image series and higher reconstruction quality. However, traditional motion-compensated approaches requiring iterative optimization of registration and reconstruction are time-consuming, while most deep learning-based methods neglect motion in the reconstruction process. We propose an unrolled deep learning framework with each iteration consisting of a groupwise diffeomorphic registration network (GRN) and a motion-augmented reconstruction network. Specifically, the whole dynamic sequence is registered at once to an implicit template which is used to generate a new set of dynamic images to efficiently exploit the full temporal information of the acquired data via the GRN. The generated dynamic sequence is then incorporated into the reconstruction network to augment the reconstruction performance. The registration and reconstruction networks are optimized in an end-to-end fashion for simultaneous motion estimation and reconstruction of dynamic images. The effectiveness of the proposed method is validated in highly accelerated cardiac cine MRI by comparing with other state-of-the-art approaches

The Influence of Large-Scale Environment on the Extremely Active Tropical Cyclone Activity in November 2019 over the Western North Pacific

In November 2019, tropical cyclone (TC) frequency over the western North Pacific reached its record high. In this study, the possible causes and formation mechanisms of that record high TC frequency are investigated by analyzing the effect of large-scale environmental factors. A comparison between the extremely active TC years and extremely inactive TC years is performed to show the importance of the large-scale environment. The contributions of several dynamic and thermodynamic environmental factors are examined on the basis of two genesis potential indexes and the box difference index that can measure the relative contributions of large-scale environmental factors to the change in TC genesis frequency. Results indicate that dynamical factors played a more important role in TC genesis in November 2019 than thermodynamic factors. The main contributions were from enhanced low-level vorticity and strong upward motion accompanied by positive anomalies in local sea surface temperature, while the minor contribution was from changes in vertical wind shear. Changes in these large-scale environmental factors are possibly related to sea surface temperature anomalies over the Pacific (e.g., strong Pacific meridional mode)

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD