Articulated dissent and immediacy: a cross-national analysis of the effects of COVID-19 lockdowns

Purpose In the midst of the COVID-19 pandemic, this study aims to explore how working remotely might impact the superior-subordinate relationship. Specifically, this study examines how immediacy explains articulated dissent, considers how an individual's attitudes toward online communication predicts immediacy and articulated dissent and compares these relationships in England, Australia and the USA. Design/methodology/approach Three nations were examined: Australia, England and the USA (n = 1,776). Surveys included demographic questions and the following measures: organizational dissent scale, perceived immediacy measure, computer-mediated immediate behaviors measure and measure of online communication attitude. Findings The results reveal supervisors' computer-mediated immediate behaviors and perceived immediacy both positively predict dissent. Some aspects of online communication attitudes positively predict computer-mediated immediate behaviors and perceived immediacy. In addition, attitudes toward online communication positively predict dissent. National culture influences some of these relationships;in each case the effects were substantively larger for the USA when compared to the other nations. Originality/value This study is the first to cross-culturally analyze dissent and immediacy. In addition, this study considers the extent to which the COVID-19 pandemic influences the superior-subordinate relationship.

Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people's lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3 18) to establish the baseline performance on three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our searched models (CovidNet3D) outperform the baseline human-designed models on three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis. Code: https://github.com/HKBU-HPML/CovidNet3D.

[Analysis of clinical characteristics of 49 cases of COVID-19].

Objective: To analyze the epidemiological and clinical characteristics, and imaging features of patients with COVID-19 in Henan Province People's Hospital. Methods: The epidemiology, clinical symptoms, laboratory and radiologic data of 49 patients with COVID-19 infection admitted to the department of infectious disease in our hospital from January 23, 2020 to February 22, 2020 were retrospectively analyzed. All analyses were performed with SPSS software, version 22.0. Results: A total of 49 patients with COVID-19 were enrolled, of which 28 were ordinary, 16 were severe, and 5 were critical in disease severity. The average ages of the 3 groups were (46±19) , (60±16) and (68±20) years, with statistical differences (P=0.015). Common symptoms at the onset were fever (41 patients), dry cough (35 patients), and fatigue (21 patients). Epidemiological investigations found that 31 (63%) patients had direct or indirect contact with confirmed cases, and 14 cases were family clustered. Laboratory test results showed that the lymphocyte counts progressively decreased [0.85 (0.5-1.6) ×10(9)/L,0.51 (0.4-0.9) ×10(9)/L and 0.43 (0.47-0.61) ×10(9)/L, respectively], while LDH [162 (145.1-203.5) U/L,265 (195.3-288.4) U/L and 387 (312.3-415.5) U/L, respectively] and D-dimer [0.15 (0.09-0.40) mg/L,0.4 (0.2-0.6) mg/L and 0.9 (0.5-1.4) mg/L, respectively] were significantly increased (P<0.05), in all the 3 groups. The levels of IL-6 [(43.2±15.4) µg/L, (78.5±31.2) µg/L and (132.4±47.9) µg/L, respectively] and IL-10 [(3.5±3.2) µg/L, (7.6±6.4) µg/L and (9.4±7.2) µg/L respectively] increased significantly with disease severity. Pulmonary imaging of ordinary patients mainly showed unilateral or bilateral multiple infiltrates, while severe and critically ill patients showed diffuse exudation and consolidation of both lungs, and a few patients showed signs of "white lungs". Conclusions: Patients with COVID-19 has a definite history of contact with diagnosed patients, and has family aggregation. The clinical symptoms were mainly fever and dry cough. Laboratory results showed that lymphocyte count, LDH, D-dimer, interleukin-6 and interleukin-10 levels had a significant correlation with the severity of the disease, which could be used as markers for disease progression and prognosis. Pulmonary imaging showed unilateral or bilateral ground glass infiltration. In severe and critically ill patients, diffuse infiltration and consolidation or even "white lung" were present.