Rice-farming areas report more anxiety across two years of the COVID-19 pandemic in China

Cultures responded to the COVID-19 pandemic differently. We investigated cultural differences in mental health during the pandemic. We found regional differences in people's reports of anxiety in China over two years from 2020 to 2021 (N = 1186). People in areas with a history of rice farming reported more anxiety than people in wheat-farming areas. Next, we explored more proximal mechanisms that could help link the distal, historical factor of rice farming to people's modern experience of anxiety. Rice areas scored higher on collectivism and tight social norms than wheat areas, and collectivism, rather than norm tightness, mediated the rice-anxiety relationship. These findings advance our understanding of the distal sources of cultural differences, the proximal mechanisms, and mental health problems during the pandemics.

How mask gap impacts discrimination and anxiety during COVID-19: A study on overseas Chinese during the first outbreak in 2020

The outbreak of the COVID-19 pandemic brought unrelenting waves of xenophobia against people representing vulnerable populations, among them those identified as Asians or more specifically as Chinese. Although previous studies have found that some discriminatory actions against overseas Chinese were closely related to mask use during the pandemic, there is not much evidence that explicates what might be the social-cultural triggers or impact of self-other mask discrepancy. The current study aims to examine how a mask use gap impacts perceived discrimination and anxiety during the first outbreak of COVID-19, and how perceived discrimination mediates the mask gap-anxiety relationship. This was operationalized by developing a new "mask gap" variable to capture the incongruent mask use norms between Chines and others around them in the host country. Data were collected from a cross-sectional sample of Chinese (n = 745) residing in 21 countries from March to May 2020 during the first wave of the pandemic. Results showed the newly explicated "mask gap" variable was associated with a higher level of anxiety. In addition, perceived discrimination mediated the mask gap-anxiety relationship. These findings advance both theoretical and practical understandings of how incongruent social norms impact discrimination and mental health during health threat events like the COVID-19 pandemic. The results also suggest important implications for both societal responses and the mental health of sojourners or immigrants during pandemics.

issues of autonomy in public health ethics: a discussion in the context of the covid-19 pandemic

In the field of public health ethics, the COVID-19 pandemic has highlighted the tension between autonomy and public health. Using CiteSpace 6.1 software and information visualization analysis, we performed a search of literature in the Web of Science core collection database using thematic words such as "public health", "ethics" and "autonomy", we found that from January 1, 2020 to May 14, 2022, discussions on the concept of "autonomy" within the field of bioethics/public health ethics were generally focused on the following topics: "informed consent", "health care policy", "health quality", "information technology", "ageism" and "elderly group". In this paper, we distill and analyze four controversial issues: how can we avoid excessive restrictions on autonomy in the name of public health/public interest? How can we protect autonomy when using digital technology? How can we protect the autonomy and rights of the elderly? How can we advance the goals of public health by promoting autonomy? The COVID-19 pandemic is a unique historical opportunity to reshape the concept of autonomy within the field of public health ethics. Although the virus has bound the fate of humanity together, a reinvented concept of autonomy based on care and community ethics holds the promise of bringing solidarity, comfort, and hope to the world in the midst of the pandemic.

Clinical Evidence and Theory of COVID-19 by Traditional Chinese Medicine.

This study aims to investigate the etiology, pathogenic properties and pathogenic characteristics of corona virus disease-2019(COVID-19)in traditional Chinese medicine(TCM),so as to provide ideas for clinical treatment based on syndrome differentiation. Efforts were made to retrive relevant literature concerning clinical studies,theoretical discussions and TCM diagnosis and treatment schemes issued by the state and various provinces,municipalities,autonomous regions and municipalities directly under the central government in relation to TCM from China Knowledge Network(CNKI)and Wanfang Database,and to analyze and summarize the etiology,pathology,theoretical viewpoints,clinical symptoms and signs,syndrome differentiation and medication rules. Currently,the common understanding of the etiology of COVID-19 in the field of TCM is the infection of "pestilential pathogen". However,there is a dispute over cold and heat or mixed understanding of cold and heat in terms of pathogenic attributes. The pathogenic factors are different from each other in dampness,toxin,dryness,fire(heat),wind,filth,depression,etc. There are various understandings on the pathogenesis including dampness,cold,heat,toxin,stasis,phlegm,stagnation,knot,dryness,filth, deficiency,blocking,collapse and asthma,etc. The etiology and pathogenesis are often mixed up. Integration of cold and heat,dryness and dampness,and other contradictory pathogens or pathogenesis is widely seen,which lacks the logicality of theoretical systems,and does not in line with the thinking characteristics of TCM on the etiology,pathogenesis,and syndrome differentiation of exogenous diseases. The main idea of medication in treatment is to diffuse the lung,clear away heat,eliminate dampness,resolve phlegm and repel foulness with aromatics. Maxing Shigantang is used as the core prescription. Chosen warm acrid drugs are mainly the ones with the effect of fragrance,removing dampness,resolving phlegm,and invigorating spleen. They are not the ones with the effects of warming yang and dissipating cold,but the combination of cold and heat,suggesting the complexity of etiology and pathogenesis. COVID-19 is categorized as plaque in TCM,and its etiology is "pestilential pathogen". This pestilential pathogen possesses not only the basic properties of toxin and filth,but also the characteristics of dampness,heat and wind. Throughout the course of the disease,phlegm,stasis,stagnation and other secondary pathogenic factors also occur. The evolution of pathogenesis is characterized by depression,blocking,and deficiency. There are more evidences that the pestilential pathogen of COVID-19 belongs to heat property no matter in the aspects of clinical manifestation,transmission law(syndrome differentiation at different stages),or in compatibility of medication.Copyright © 2021, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Clinical Evidence and Theory of COVID-19 by Traditional Chinese Medicine.

This study aims to investigate the etiology, pathogenic properties and pathogenic characteristics of corona virus disease-2019(COVID-19)in traditional Chinese medicine(TCM),so as to provide ideas for clinical treatment based on syndrome differentiation. Efforts were made to retrive relevant literature concerning clinical studies,theoretical discussions and TCM diagnosis and treatment schemes issued by the state and various provinces,municipalities,autonomous regions and municipalities directly under the central government in relation to TCM from China Knowledge Network(CNKI)and Wanfang Database,and to analyze and summarize the etiology,pathology,theoretical viewpoints,clinical symptoms and signs,syndrome differentiation and medication rules. Currently,the common understanding of the etiology of COVID-19 in the field of TCM is the infection of "pestilential pathogen". However,there is a dispute over cold and heat or mixed understanding of cold and heat in terms of pathogenic attributes. The pathogenic factors are different from each other in dampness,toxin,dryness,fire(heat),wind,filth,depression,etc. There are various understandings on the pathogenesis including dampness,cold,heat,toxin,stasis,phlegm,stagnation,knot,dryness,filth, deficiency,blocking,collapse and asthma,etc. The etiology and pathogenesis are often mixed up. Integration of cold and heat,dryness and dampness,and other contradictory pathogens or pathogenesis is widely seen,which lacks the logicality of theoretical systems,and does not in line with the thinking characteristics of TCM on the etiology,pathogenesis,and syndrome differentiation of exogenous diseases. The main idea of medication in treatment is to diffuse the lung,clear away heat,eliminate dampness,resolve phlegm and repel foulness with aromatics. Maxing Shigantang is used as the core prescription. Chosen warm acrid drugs are mainly the ones with the effect of fragrance,removing dampness,resolving phlegm,and invigorating spleen. They are not the ones with the effects of warming yang and dissipating cold,but the combination of cold and heat,suggesting the complexity of etiology and pathogenesis. COVID-19 is categorized as plaque in TCM,and its etiology is "pestilential pathogen". This pestilential pathogen possesses not only the basic properties of toxin and filth,but also the characteristics of dampness,heat and wind. Throughout the course of the disease,phlegm,stasis,stagnation and other secondary pathogenic factors also occur. The evolution of pathogenesis is characterized by depression,blocking,and deficiency. There are more evidences that the pestilential pathogen of COVID-19 belongs to heat property no matter in the aspects of clinical manifestation,transmission law(syndrome differentiation at different stages),or in compatibility of medication.Copyright © 2021, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

CAPRL Scoring System for Prediction of 30-day Mortality in 949 Patients with Coronavirus Disease 2019 in Wuhan, China: A Retrospective, Observational Study

Background:Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces.Methods:In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality.Results:The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively.Conclusions:We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.

A network pharmacology approach to explore the mechanism of action of Yiqi Fumai lyophilized injection in the treatment of novel coronavirus disease

Background: Shengmai decoction, which has been included in the diagnosis and treatment of coronavirus disease 2019 (COVID-19), is effective in the early treatment of patients with severe COVID-19. Yiqi Fumai lyophilized injection (YQFM) is a modern Chinese medicine preparation of the Shengmai decoction. The mechanism of its intervention at the molecular level in the severe stage of COVID-19 remains unclear. Therefore, it is necessary to investigate the mechanism of YQFM in the treatment of patients with severe COVID-19. Methods: The corresponding target genes of the main active ingredients in YQFM and COVID-19 were obtained by using multiple databases and literature retrieval. A protein-protein interaction network was constructed, and enrichment analysis of the target was performed using Cytoscape 3.8.1. Lastly, the docking of all the identified compounds with angiotensin-converting enzyme II was confirmed by applying molecular docking technology. Results: YQFM has anti-inflammatory effects on RAW267.4 macrophages. The main active compounds of YQFM are all effective anti-inflammatory agents, and these active compounds also show beneficial physiological functions, such as anti-oxidation, anti-bacterial, and anticancer activities. Gene Ontology analysis showed enrichment in the following pathways: lipopolysaccharides, interleukins, NF-kappa B, interleukin-2 and others, revealing that YQFM may play a role in the treatment of patients with severe COVID-19 through these pathways. Conclusion: YQFM has multicomponent and multitarget characteristics, and it could reduce lung injury by inhibiting inflammatory reactions, promoting antiviral activities, and regulating immunity, among other functions, to treat patients with severe COVID-19.

AMDetector: Detecting Large-Scale and Novel Android Malware Traffic with Meta-learning

In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

GBLNet: Detecting Intrusion Traffic with Multi-granularity BiLSTM

Detecting and intercepting malicious requests are some of the most widely used ways against attacks in the network security, especially in the severe COVID-19 environment. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a more general and rigorous detection method is required. In this paper, we formulate the problem of detecting malicious requests as a temporal sequence classification problem, and propose a novel deep learning model namely GBLNet, girdling bidirectional LSTM with multi-granularity CNNs. By connecting the shadow and deep feature maps of the convolutional layers, the malicious feature extracting ability is improved on more detailed functionality. Experimental results on HTTP dataset CSIC 2010 demonstrate that GBLNet can efficiently detect intrusion traffic with superior accuracy and evaluating speed, compared with the state-of-the-arts. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Generative Facial Prior and Semantic Guidance for Iterative Face Inpainting

Image inpainting techniques have been greatly improved by relying on structure and texture priors. However, damaged original images or rough predictions cannot provide sufficient texture information and accurate structural priors, leading to a drop in image quality. Moreover, from the perspective of human visual perception, it is important to pay attention to facial symmetry and facial attribute consistency. In this paper, we present a face inpainting system with iteration structure, guided by generative facial priors contained in pretrained GANs and predicted semantic information. Specifically, generative facial priors generated by the GAN inversion techniques introduce sufficient textures and features to assist inpainting;semantic maps are able to provide facial structural information and semantic categories of different pixels for face reconstruction. In particular, we iteratively refine images multiple times, updating semantic maps at each iteration. The Weighted Prior-Guidance Modulation layer (WPGM) is devised for incorporating priors into networks through spatial modulation. We also propose facial feature self-symmetry loss to constrain the symmetry of faces in feature space. Experiments on CelebA-HQ and LaPa datasets demonstrate the superiority of our model for facial detail and attribute consistency. Meanwhile, under the background of COVID-19, it is worth trying recognition via inpainting to deal with recognition challenges brought by mask occlusion. Relevant experiments show that our inpainting model does help to recognition tasks to a certain degree, with higher accuracy.

Travel Mode Choice Analysis with Shared Mobility in Context of COVID-19

To analyze the impact of COVID-19 on the travel mode choice behavior with diverse shared mobility services, this study designed the stated preference (SP) questionnaire for the multi-modal transportation system which include conventional travel modes, ride hailing, ride sharing, car sharing, and bike sharing. The mixed Logit models with panel data were proposed to investigate the travel mode choices before and during COVID-19. The influence differences of explanatory variables are compared, and the joint effects of perceived pandemic severity and mode choice inertia are examined. Based on the elasticity analysis, the mode choice preferences are predicted corresponding to different management policies under COVID-19 pandemic. The results indicate that the perception to pandemic severity has significant impacts on the ridership of ride sharing and car sharing, and the mode choice inertia obviously affects the usage of ride hailing, car sharing, and bike sharing. When the perceived pandemic severity reduces to 30%~50%, the strategy of increasing parking charge to 1.6~3.0 times would reduce the usage of private car to pre-pandemic condition, and the car sharing with lower close contact risk could become a main substitute. When the perceived pandemic severity is higher than 60%, the strategy of increasing the travel safety of ride sharing to 1.4~3.6 times would improve the ridership. Copyright © 2022 by Science Press.

A Privacy Preserving Federated Learning Framework for COVID-19 Vulnerability Map Construction

This paper presents a federated learning (FL) framework that uses multiple self-reporting crowdsourcing mobile and web apps to collaboratively construct a fine-grained COVID-19 vulnerability prediction map. The use of FL provides a reliable prediction by aggregating training results from multiple apps, while at the same time circumventing data privacy regulations that prevent user information from multiple apps to be shared with each other. Such a fine-grained vulnerability map identifies early on high-risk areas, helping to reduce the spread of the disease. To mitigate data bias from each self-reporting app, an adaptive worker selection algorithm that leverages neighbouring datasets to obtain a balanced data distribution is proposed. Further, a differential privacy scheme is adopted to protect user information. The simulation results show that the proposed framework outperforms the widely used FedAvg FL algorithm by 6% on prediction accuracy while preserving user privacy.

An Earth-mass planet in a time of COVID-19: KMT-2020-BLG-0414Lb

We report the discovery of KMT-2020-BLG-0414Lb, with a planet-to-host mass ratio q (2) = 0.9-1.2 x 10(-5) = 3-4 circle plus at 1 sigma, which is the lowest mass-ratio microlensing planet to date. Together with two other recent discoveries (4 less than or similar to q/q (circle plus) less than or similar to 6), it fills out the previous empty sector at the bottom of the triangular (log s, log q) diagram, where s is the planet-host separation in units of the angular Einstein radius theta E. Hence, these discoveries call into question the existence, or at least the strength, of the break in the mass-ratio function that was previously suggested to account for the paucity of very low-q planets. Due to the extreme magnification of the event, A (max) similar to 1450 for the underlying single-lens event, its light curve revealed a second companion with q (3) similar to 0.05 and ;log s (3);similar to 1, i.e., a factor similar to 10 closer to or farther from the host in projection. The measurements of the microlens parallax pi (E) and the angular Einstein radius theta E allow estimates of the host, planet and second companion masses, (M (1), M (2), M (3)) similar to (0.3 M (circle dot), 1.0 M (circle plus), 17 M-J ), the planet and second companion projected separations, (a (perpendicular to,2), a (perpendicular to,3)) similar to (1.5, 0.15 or 15) au, and system distance D (L) similar to 1 kpc. The lens could account for most or all of the blended light (I similar to 19.3) and so can be studied immediately with high-resolution photometric and spectroscopic observations that can further clarify the nature of the system. The planet was found as part of a new program of high-cadence follow-up observations of high-magnification events. The detection of this planet, despite the considerable difficulties imposed by COVID-19 (two KMT sites and OGLE were shut down), illustrates the potential utility of this program.

Negative Correlation Between Virus-Related Content Popularity and Epidemic Spread

The coronavirus disease 2019 (COVID-19) has recently attracted extensive attention due to its serious impact on public health worldwide. In this paper, we study and verify that the popularity of virus-related content has a negative correlation with the epidemic spread by means of statistical analysis. Inspired by this result, a practical solution of recommender system is proposed for pushing virus-related content, aiming to gain insight about the newly discovered virus for people and thus reduce the epidemic spread to the utmost extent. First, we formulate the optimization of recommendation policy subject to quality of experience (QoE) loss constraints as a finite-horizon Constrained Markov Decision Problem (CMDP). To solve this problem, then, we present both enumeration and heuristic methods, from perspectives of achieving optimal recommendation policy and reducing computational complexity, respectively. Finally, our simulations validate the benefit of our solution by showing that to recommend virus-related content following our strategy does help slow down the spread of the epidemic.

How Does the Market React to Corporate Philanthropic Behavior? -evidence from the COVID-19 Pandemic Shock

Based on 1,130 listed Chinese firms' charitable donation data during the COVID-19, this paper used the Event Study to examine market reactions to the epidemic and utilized OLS and Heckman two-stage models to investigate the impact of charitable donations on corporate market performance. Results show that greater corporate charitable material and medical donations result in more favorable short-term market reaction but weaker in the long term. Moreover, the low-leveraged, non-pharmaceutical, and non-SOEs can obtain better short-term performance through philanthropic donations. Findings suggest that the negative market sentiment from the COVID-19 cannot be offset by the short-term positive effects of corporate donations.

Active components and mechanism of Tibetan medicine Gentiana straminea against cytokine storm

Objective: To analyze the main active components and possible mechanism of Mahuaqinjiao (Gentiana straminea) in the preventive treatment of cytokine storm by network pharmacology. Methods: The therapeutic targets for cytokine storms and its active ingredients of G. straminea were searched and collected from the database of TCMSP and GeneCards;The regulatory network map of active component cytokine storm targets of G. straminea was constructed by using Cytoscape 3.6.1;The network map of protein-protein interaction was constructed to screen the core target, and the gene ontology (GO) function enrichment analysis and KEGG pathway enrichment analysis of the core protein were carried out by using the online database of DAVID. Results: After screening, 14 active components and 31 effective targets for prevention and treatment of cytokine storm were obtained, mainly including IL6, STAT3, Jun, AKT1, EGFR, MMP9, VEGFA, IL-1β, IL-10, and other core targets. Go analysis showed that G. straminea mainly involved in the regulation of apoptosis, gene expression, cytokine activity, positive regulation of nitric oxide biosynthesis, growth factor activity, immune response and other processes. KEGG pathway analysis showed that the main pathways related to cytokine storm were T-cell receptor signaling pathway, JAK STAT signaling pathway, HIF-1 signaling pathway, tumor necrosis factor signaling pathway, Toll like receptor signaling pathway, PI3K Akt signaling pathway and other core target pathways. Conclusion: Based on the data mining of network pharmacology, the potential active components and possible mechanism targets of G. straminea against cytokine storm were predicted preliminarily, in order to provide reference for the application of G. straminea in the prevention and control of cytokine storm, and provide ideas for the development of new uses of G. straminea.

Strategy and thinking of the development and review policies of traditional Chinese medicine (TCM) new drugs taking "three Chinese patent medicines and three TCM prescriptions" as examples

A large number of traditional Chinese medicine (TCM) prescriptions have played an active role in the prevention and treatment of emerging epidemic diseases, which contain huge potential of research and development (R&D). However, in recent years, few TCM new drugs have been approved for market and the industry is in a downturn. With examples of the recent drug review policies and the "three Chinese patent medicines and three TCM prescriptions" for Corona Virus Disease 2019 (COVID-19), this article proposed a series of strategies from the perspectives of review and R&D of TCM new drugs as well as resource allocation. The strategies are improving the R&D layout of TCM new drugs, clarifying the evaluation criteria of human experience evidence, supervising the syndrome indications for TCM new drugs other than diseases, implementing differentiation competitive strategy, applying multi-disciplinary methods in prescription screening, applying integrated research design, increasing investment in high-quality clinical research, and integrating resources to break through international barriers. © 2020, Chinese Journal of New Drugs Co. Ltd. All right reserved.