Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic. © 2021 Association for Computing Machinery.

Asymmetric volatility spillover among global oil, gold, and Chinese sectors in the presence of major emergencies

The volatility of international crude oil and gold markets has affected stock markets through several economic channels, and the impact tends to be more evident with the appearance of emergencies. However, the volatility linkages between commodities and Chinese sector stocks in the presence of emergencies are understudied. To examine the asymmetric relationship and time-varying connectedness between commodities and Chinese sector stocks, this paper first employs GJR-GARCH to capture the realized volatility of international oil, gold, and Chinese sector stocks. Secondly, we decompose the realized volatility of international oil and gold into bad and good volatility and then employ the TVP-VAR-DY approach to obtain the connectedness index. The final result shows asymmetric volatility spillover among oil, gold, and Chinese sector stocks. During the COVID-19 outbreak, the gold good volatility transmission is intenser than bad volatility. Thirdly, the analysis is also carried out under different subperiods. They include three international events: the global financial crisis and the European debt crisis, the oil crisis, and COVID-19. The result reveals heterogeneity exists in the impact of international oil and gold on the Chinese sector stocks under different emergencies. These findings are of great significance for policymakers to improve the sector management under the impact of different emergencies and for investors to design diversified portfolios according to the commodity-sector risk spillover effects. © 2023 Elsevier Ltd

Research progress in antiviral therapy for novel coronavirus pneumonia.

Since the outbreak of novel coronavirus pneumonia (COVID-19), a number of clinical studies have been carried out globally in order to explore efficacy and safety of drugs for novel coronavirus (2019-nCoV). These studies were mainly focused on drugs with anti-2019-nCoV activity tested in vitro and those previously used for the treatment of SARS and Middle East respiratory syndrome, including remdesivir, lopinavir/ritonavir, chloroquine, hydroxychloroquine, arbidol, interferon, ribavirin, and etc. The recent clinical studies on anti-2019-nCoV drugs are reviewed in this article, but the current research results are inconsistent, which are insufficient to constitute evidence for the efficacy and safety of these drugs in the treatment of COVID-19. In the absence of specific antiviral agents, remdesivir can be a treatment option for patients with critical illness or rapid progress. Some clinical studies are still in progress. We are looking forward to more large-scale and multicenter clinical trials to provide safe and effective evidence for antiviral treatment in the future.Copyright © 2020 by the Chinese Medical Association.

Reconfigurable and In-sensor Computing Pb-free Perovskite Array towards Intelligent X-ray Imaging

Conventional X-ray imaging architectures feature data redundancy and hardware consumption due to the separated sensory terminal and computing units. In-sensor computing architectures is promising to overcome such drawbacks. However, its realization in X-ray range remains elusive. We propose ion distribution induced reconfigurable mechanism, and demonstrate the first X-ray band in-sensor computing array based on Pb-free perovskite. Redistribution of Br- ion in perovskite induces the switching of PN and NP modes under electrical pooling. X-ray detection sensitivity can be switched between two stable self-power sensing modes with 4373±298 and -7804±429 mu mathrm{CGy}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{cm}{-2} respectively, which are superior than that of commercial a-Se detectors (20 mu mathrm{C} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}}{}{-1} mathrm{c} mathrm{m}{-2}). Both modes exhibit low detection limit of 48.4 mathrm{n} mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}, which is two orders lower than typical medical dose rate of 5.5 mu mathrm{G} mathrm{y}-{ mathrm{a} mathrm{i} mathrm{r}} mathrm{s}{-1}. The perovskite array sensors can integrate with thin film transistors (TFTs) with low-temperature (80oC) process with good uniformity. An in-sensor computing algorithm of attention mechanism is performed on array sensors for chest X-ray images COVID-19 recognition, which enables an accuracy improvement up to 98.2%. Our results can pave the way for future intelligent X-ray imaging. © 2022 IEEE.

Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concernedwith from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic.

Reducing latency and bandwidth for video streaming using keypoint extraction and digital puppetry

COVID-19 has made video communication one of the most important modes of information exchange. While extensive research has been conducted on the optimization of the video streaming pipeline, in particular the development of novel video codecs, further improvement in the video quality and latency is required, especially under poor network conditions. This paper proposes an alternative to the conventional codec through the implementation of a keypoint-centric encoder relying on the transmission of keypoint information from within a video feed, as shown in Figure 1. The decoder uses the streamed keypoints to generate a reconstruction preserving the semantic features in the input feed. Focusing on video calling applications, we detect and transmit the body pose and face mesh information through the network, which are displayed at the receiver in the form of animated puppets. Using efficient pose and face mesh detection in conjunction with skeleton-based animation, we demonstrate a prototype requiring lower than 35 kbps bandwidth, an order of magnitude reduction over typical video calling systems. The added computational latency due to the mesh extraction and animation is below 120ms on a standard laptop, showcasing the potential of this framework for real-time applications. The code for this work is available at http://github.com/shubhamchandak94/digital-puppetry/and the full version is available on arXiv [1]. © 2021 IEEE.

Supercoiling Structure-Based Design of a Trimeric Coiled-Coil Peptide with High Potency against HIV-1 and Human beta-Coronavirus Infection

Hexameric structure formation through packing of three C-terminal helices and an N-terminal trimeric coiled-coil core has been proposed as a general mechanism of class I enveloped virus entry. In this process, the C-terminal helical repeat (HR2) region of viral membrane fusion proteins becomes transiently exposed and accessible to N-terminal helical repeat (HR1) trimer-based fusion inhibitors. Herein, we describe a mimetic of the HIV-1 gp41 HR1 trimer, N3G, as a promising therapeutic against HIV-1 infection. Surprisingly, we found that in addition to protection against HIV-1 infection, N3G was also highly effective in inhibiting infection of human beta-coronaviruses, including MERS-CoV, HCoV-OC43, and SARS-CoV-2, possibly by binding the HR2 region in the spike protein of beta-coronaviruses to block their hexameric structure formation. These studies demonstrate the potential utility of anti-HIV-1 HR1 peptides in inhibiting human beta-coronavirus infection. Moreover, this strategy could be extended to the design of broad-spectrum antivirals based on the supercoiling structure of peptides.

[Preliminary analysis on COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province].

Objective: To analyze the characteristics of COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province. Methods: The daily incidence data and case information of COVID-19 were collected from the official websites of provincial and municipal health commissions. The morbidity rate, severity rate, case-fatality rate, and spread ratio of COVID-19 were calculated. Results: As of 20 March, 2020, a total of 12 941 cases of COVID-19 had been conformed, including 116 deaths, and the average morbidity rate, severity rate and case-fatality rate were 0.97/100 000, 13.5% and 0.90%, respectively. The morbidity rates in Zhejiang (2.12/100 000), Jiangxi (2.01/100 000) and Beijing (1.93/100 000) ranked top three. The characteristics of COVID-19 case spectrum varied from province to province. The first three provinces (autonomous region, municipality) with high severity rates were Tianjin (45.6%), Xinjiang (35.5%) and Heilongjiang (29.5%). The case-fatality rate was highest in Xinjiang (3.95%), followed by Hainan (3.57%) and Heilongjiang (2.70%). The average spread ratio was 0.98 and the spread intensity varied from province to province. Tibet had the lowest spread ratio (0), followed by Qinghai (0.20) and Guangdong (0.23). Conclusion: The intervention measures were effective in preventing the spread of COVID-19 and improved treatment effect in China. However, there were significant differences among different regions in severity, case-fatality rate and spread ratio.