Phase-separated nucleocapsid protein of SARS-CoV-2 suppresses cGAS-DNA recognition by disrupting cGAS-G3BP1 complex.

Currently, the incidence and fatality rate of SARS-CoV-2 remain continually high worldwide. COVID-19 patients infected with SARS-CoV-2 exhibited decreased type I interferon (IFN-I) signal, along with limited activation of antiviral immune responses as well as enhanced viral infectivity. Dramatic progresses have been made in revealing the multiple strategies employed by SARS-CoV-2 in impairing canonical RNA sensing pathways. However, it remains to be determined about the SARS-CoV-2 antagonism of cGAS-mediated activation of IFN responses during infection. In the current study, we figure out that SARS-CoV-2 infection leads to the accumulation of released mitochondria DNA (mtDNA), which in turn triggers cGAS to activate IFN-I signaling. As countermeasures, SARS-CoV-2 nucleocapsid (N) protein restricts the DNA recognition capacity of cGAS to impair cGAS-induced IFN-I signaling. Mechanically, N protein disrupts the assembly of cGAS with its co-factor G3BP1 by undergoing DNA-induced liquid-liquid phase separation (LLPS), subsequently impairs the double-strand DNA (dsDNA) detection ability of cGAS. Taken together, our findings unravel a novel antagonistic strategy by which SARS-CoV-2 reduces DNA-triggered IFN-I pathway through interfering with cGAS-DNA phase separation.

Case study of the Beijing 2022 Olympic Winter Games: Implications for global mass gathering events amidst the COVID-19 pandemic.

Objective: This study aimed to evaluate the public health countermeasures against coronavirus disease 2019 (COVID-19) that are important for organizing mass gathering events (MGEs) during a pandemic and to identify the practices suitable for application at future MGEs. Methods: This study analyzed data from the Beijing 2022 Olympic Winter Games. The aforementioned analysis was conducted from the viewpoints of overseas stakeholders and Chinese residents. The comprehensive set of countermeasures established to prevent the transmission of the COVID-19 pandemic comprised the bubble strategy, the three-layer testing strategy (pre-departure testing, testing at the airport, and daily screening), the mandatory wearing of N95 masks, and mandatory vaccination. Findings: A total of 437 positive cases within the bubble were reported during the Games, of which 60.6% were detected through screening at the airport and 39.4% were detected through routine screening. Nearly, 92.0% of the positive cases were detected within 7 days of arrival in China, and 80.8% of the cases had already been identified before the Opening Ceremony of the Games. Outside the bubble, no Games stakeholders were infected and no spectator contracted COVID-19. The bubble strategy, the three-layer testing strategy, the mandatory wearing of N95 masks, and mandatory vaccination are promising countermeasures to prevent the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during MGEs. Conclusion: Public health countermeasures introduced during the Beijing 2022 Olympic Winter Games were proven to be useful. The success in delivering and organizing the Games instills confidence and leaves a public health legacy for future MGEs amid the pandemic of COVID-19 or future emerging infectious diseases.

SARS-CoV-2 BA.2 (Omicron) variant infection in pediatric liver transplanted recipients and cohabitants during 2022 Shanghai outbreak: a prospective cohort.

BACKGROUND: The Omicron variant BA.2 was the dominant variant in the COVID-19 outbreak in Shanghai since March 2022. We aim to investigate the characteristics of SARS-CoV-2 Omicron variant infection in pediatric liver-transplanted recipients. METHODS: We conducted a single-center, prospective, observational, single-arm study. We enrolled pediatric liver-transplanted patients infected with the Omicron variant BA.2 from March 19th to October 1st, 2022 and analyzed their demographic, clinical, laboratory, and outcome data. The management of COVID-19 was conducted according to the 9th trial edition of the Chinese guideline. The immunosuppressive therapy was tailored considering the patients' infection developments and liver functions. RESULTS: Five children were included. The primary diseases included Niemann-Pick disease, propionic acidemia, decompensated cirrhosis, biliary atresia, and Crigler-Najjar syndrome type I. All of the patients were onset with fever before or when getting RNA-positive results at the age of 3 (Range: 1-13) years. The infection duration was 29 (Range: 18-40) days. Three and two children were diagnosed with mild and moderate COVID-19 respectively. Two patients were tested RNA-positive within 14 days after having been tested negative. The immunosuppressants were paused or extenuated in four patients. Eight of all nine cohabitants were injected with at least two doses of inactivated SARS-CoV-2 vaccine. The disease courses were significantly longer than the patients (P < 0.05). CONCLUSIONS: Post-transplant immunosuppression slows down the virus clearance and increases the risk of relapse but does not affect symptom duration or infection severity in pediatric patients. Patients can usually gain a favorable outcome and prognosis by extenuating immunosuppressants.

Relationships between urban form and air quality: A reconsideration based on evidence from China's five urban agglomerations during the COVID-19 pandemic.

The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O3, NO2, PM2.5, PM10, SO2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.

Low COVID-19 vaccine coverage and guardian acceptance among pediatric transplant recipients.

To investigate COVID-19 vaccine coverage in immunosuppressed children, assess guardians' intention to vaccinate children, and determine reasons and associated factors. In addition, we attempted to capture the characteristics of them with Omicron. We obtained the vaccination coverage and guardian vaccine acceptance among pediatric transplant recipients through a web-based questionnaire conducted from April 12 to 28, 2022, and performed the statistical analysis. Seven organ transplant recipient children with Omicron were also clinically analyzed. The three-dose vaccine coverage for liver transplant (n = 563) and hematopoietic stem cell transplantation (n = 122) recipient children was 0.9% and 4.9%, and guardian vaccine acceptance was 63.8%. Independent risk factors for vaccine acceptance were the child's age, geographic location, type of transplant, guardian's vaccination status, guardian's level of distress about epidemic events, guardian's risk perception ability, anxiety, and knowledge of epidemic control. The main reasons for vaccine hesitancy were fear of vaccine-induced adverse events and doubts about efficacy. Ultimately, most children infected with Omicron have mild or no symptoms and are infected by intra-family. Since vaccine coverage and guardian acceptance are lowest among liver transplant children, and the infected are mainly intra-family, we should devise more targeted education and vaccination instructions for their guardians.

Novel insights into NOD-like receptors in renal diseases.

Nucleotide-binding oligomerization domain-like receptors (NLRs), including NLRAs, NLRBs (also known as NAIPs), NLRCs, and NLRPs, are a major subfamily of pattern recognition receptors (PRRs). Owing to a recent surge in research, NLRs have gained considerable attention due to their involvement in mediating the innate immune response and perpetuating inflammatory pathways, which is a central phenomenon in the pathogenesis of multiple diseases, including renal diseases. NLRs are expressed in different renal tissues during pathological conditions, which suggest that these receptors play roles in acute kidney injury, obstructive nephropathy, diabetic nephropathy, IgA nephropathy, lupus nephritis, crystal nephropathy, uric acid nephropathy, and renal cell carcinoma, among others. This review summarises recent progress on the functions of NLRs and their mechanisms in the pathophysiological processes of different types of renal diseases to help us better understand the role of NLRs in the kidney and provide a theoretical basis for NLR-targeted therapy for renal diseases.

Emoji use in China: popularity patterns and changes due to COVID-19.

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information: The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Effects of urban form on air quality: A case study from China comparing years with normal and reduced human activity due to the COVID-19 pandemic.

This study explored the dynamic and complex relationships between air quality and urban form when considering reduced human activities. Applying the random forest method to data from 62 prefecture-level cities in China, urban form-air quality relationships were compared between 2015 (a normal year) and 2020 (which had significantly reduced air pollution due to COVID-19 lockdowns). Significant differences were found between these two years; urban compactness, shape, and size were of prime importance to air quality in 2020, while fragmentation was the most critical factor in improving air quality in 2015. An important influence of traffic mode was also found when controlling air pollution. In general, in the pursuit of reducing air pollution across society, the best urban forms are continuous and compact with reasonable building layouts, population, and road densities, and high forest area ratios. A polycentric urban form that alleviates the negative impacts of traffic pollution is preferable. Urban development should aim to reduce air pollution, and optimizing the effects of urban form on air quality is a cost-effective way to create better living environments. This study provides a reference for decision-makers evaluating the effects of urban form on air pollution emission, dispersion, and concentration in the post-pandemic era.

Structural Evolution and Community Detection of China Rail Transit Route Network

How to improve the partial or overall performance of rail transit route network, strengthen the connection between different rail network stations, and form corresponding communities to resist the impact of sudden or long-term external factors has earned a lot of attention recently. However, the corresponding research studies are mostly based on the rail network structure, and the analysis and exploration of the community formed by the stations and its robustness are not enough. In this article, the evolution of the China rail transit route network (CRTRN) from 2009 to 2022 is taken as the research object, and its complex network characteristics, BGLL model-based community division, and multi disturbance strategies for network robustness are analyzed in depth to better understand and optimize the rail network structure to further effectively improve the efficiency of the public transport system. It is found that CRTRN is gradually expanding following the southwest direction (with the migration distance of nearly 200 km), the distribution of routes is more balanced, and the number of network communities is steadily decreasing (it dropped from 30 communities in 2009 to 25 in 2019), making various regions become closely connected. However, it can also be found that during the COVID-19 pandemic, the CRTRN is strongly affected, and the network structure becomes relatively loose and chaotic (the number of communities became 30). To protect the railway networks, the CRTRN system should pay more attention to stations with high node degree values;if they get disturbed, more areas will be affected. The corresponding research conclusions can provide some theoretical and practical support for the construction of the rail transit network in China.

Immune Persistence against SARS-CoV-2 after Primary and Booster Immunization in Humans: A Large-Scale Prospective Cohort Study.

Amid the ongoing global COVID-19 pandemic, limited literature exists on immune persistence after primary immunization and the immunogenic features of booster vaccines administered at different time intervals. Therefore, this study aimed to determine the immune attenuation of neutralizing antibodies against the SARS-CoV-2 wild-type strain, and Delta and Omicron variants 12 months after the primary administration of the COVID-19 inactivated vaccine and evaluate the immune response after a booster administration at different time intervals. A total of 514 individuals were followed up after primary immunization and were vaccinated with a booster. Neutralizing antibodies against the wild-type strain and Delta and Omicron variant spike proteins were measured using pseudovirus neutralization assays. The geometric mean titers (GMTs) after the primary and booster immunizations were 12.09 and 61.48 for the wild-type strain, 11.67 and 40.33 for the Delta variant, and 8.51 and 29.31 for the Omicron variant, respectively. The GMTs against the wild-type strain declined gradually during the 12 months after the primary immunization, and were lower against the two variants. After implementing a booster immunization with a 6 month interval, the GMTs against the wild-type strain were higher than those obtained beyond the 7 month interval; however, the GMTs against the two variants were not statistically different across 3-12 month intervals. Overall, SARS-CoV-2 variants showed remarkable declines in immune persistence, especially against the Omicron variant. The booster administration interval could be shortened to 3 months in endemic areas of the Omicron variant, whereas an appropriate prolonging of the booster administration interval did not affect the booster immunization effect.

Targeting Selective Autophagy as a Therapeutic Strategy for Viral Infectious Diseases.

Autophagy is an evolutionarily conserved lysosomal degradation system which can recycle multiple cytoplasmic components under both physiological and stressful conditions. Autophagy could be highly selective to deliver different cargoes or substrates, including protein aggregates, pathogenic proteins or superfluous organelles to lysosome using a series of cargo receptor proteins. During viral invasion, cargo receptors selectively target pathogenic components to autolysosome to defense against infection. However, viruses not only evolve different strategies to counteract and escape selective autophagy, but also utilize selective autophagy to restrict antiviral responses to expedite viral replication. Furthermore, several viruses could activate certain forms of selective autophagy, including mitophagy, lipophagy, aggrephagy, and ferritinophagy, for more effective infection and replication. The complicated relationship between selective autophagy and viral infection indicates that selective autophagy may provide potential therapeutic targets for human infectious diseases. In this review, we will summarize the recent progress on the interplay between selective autophagy and host antiviral defense, aiming to arouse the importance of modulating selective autophagy as future therapies toward viral infectious diseases.

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted.

BACKGROUND: Contact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic, the regular contact patterns of the population have been disrupted due to social distancing both imposed by the authorities and individual choices. Many studies have focused on age-mixing patterns before the COVID-19 pandemic, but they provide very little information about the mixing patterns in the COVID-19 era. In this study, we aim at quantifying human heterogeneous mixing patterns immediately after lockdowns implemented to contain COVID-19 spread in China were lifted. We also provide an illustrative example of how the collected mixing patterns can be used in a simulation study of SARS-CoV-2 transmission. METHODS AND RESULTS: In this work, a contact survey was conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a mathematical model of SARS-CoV-2 transmission. Study participants reported 2.3 contacts per day (IQR: 1.0-3.0) and the mean per-contact duration was 7.0 h (IQR: 1.0-10.0). No significant differences in average contact number and contact duration were observed between provinces, the number of recorded contacts did not show a clear trend by age, and most of the recorded contacts occurred with family members (about 78%). The simulation study highlights the importance of considering age-specific contact patterns to estimate the COVID-19 burden. CONCLUSIONS: Our findings suggest that, despite lockdowns were no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation.

Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer.

The sorting of Construction and Demolition (C&D) waste is a critical step to linking the recycling system and to the macro prediction, which helps to promote the development of the circular economy. Moreover, the effective classification and automated separation process will also help to stop the spreading of pathogenic organisms, such as virus and bacteria, by minimizing human intervention in the sorting process, while also helping to prevent further contamination by COVID-19 virus. This study aims to develop an efficient method to sort C&D waste through deep learning combined with knowledge transfer approach. In this paper, CVGGNet models, that is four VGG structures (VGGNet-11, VGGNet-13, VGGNet-16, and VGGNet-19), based on knowledge transfer combined with the technology of data augmentation and cyclical learning rate, are proposed to classify ten types of C&D waste images. Results show that 2.5 × 10-4, 1.8 × 10-4, 0.8 × 10-4, and 1.0 × 10-4 are the optimum learning rate for CVGGNet-11, CVGGNet-13, CVGGNet-16, and CVGGNet-19, respectively. Knowledge transfer helped shorten the training time from 1039.45 s to 991.05 s, and while it improved the performance of the CVGGNet-11 model in training, validation, and test datasets. The average training time increases as the number of the layers in the CVGGNet architecture rises: CVGGNet-11 (991.05 s) ˂ CVGGNet-13 (1025.76 s) ˂ CVGGNet-16 (1090.48 s) ˂ CVGGNet-19 (1337.81 s). Compared to other CVGGNet models, CVGGNet-16 showed an excellent performance in various C&D waste types, in terms of accuracy (76.6%), weighted average precision (76.8%), weighted average recall (76.6%), weighted average F1-score (76.6%) and micro average ROC (87.0%). In addition, the t-distributed Stochastic Neighbor Embedding (t-SNE) approach can reduce the dataset to a lower dimension and distinctly separate each type of C&D waste. This study demonstrates the good performance of CVGGNet models that can be used to automatically sort most of the C&D waste, paving the way for better C&D waste management.

First report of de novo assembly and annotation from brain and blood transcriptome of an anadromous shad, Alosa sapidissima.

OBJECTIVES: American shad (Alosa sapidissima) is an important migratory fish under Alosinae and has long been valued for its economic, nutritional and cultural attributes. Overfishing and barriers across the passage made it vulnerable to sustain. To protect this valuable species, aquaculture action plans have been taken though there are no published genetic resources prevailing yet. Here, we reported the first de novo assembled and annotated transcriptome of A. sapidissima using blood and brain tissues. DATA DESCRIPTION: We generated 160,481 and 129,040 non-redundant transcripts from brain and blood tissues. The entire work strategy involved RNA extraction, library preparation, sequencing, de novo assembly, filtering, annotation and validation. Both coding and non-coding transcripts were annotated against Swissprot and Pfam datasets. Nearly, 83% coding transcripts were functionally assigned. Protein clustering with clupeiform and non-clupeiform taxa revealed ~ 82% coding transcripts retained the orthologue relationship which improved confidence over annotation procedure. This study will serve as a useful resource in future for the research community to elucidate molecular mechanisms for several key traits like migration which is fascinating in clupeiform shads.

Emoji use in China: popularity patterns and changes due to COVID-19

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users’ usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage;i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19.

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events. Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19. Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic. Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant "rebound effect" by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003). Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Compound screen identifies the small molecule Q34 as an inhibitor of SARS-CoV-2 infection.

The COVID-19 outbreak poses a serious threat to global public health. Effective countermeasures and approved therapeutics are desperately needed. In this study, we screened a small molecule library containing the NCI-DTP compounds to identify molecules that can prevent SARS-CoV-2 cellular entry. By applying a luciferase assay-based screening using a pseudotyped SARS-CoV-2-mediated cell entry assay, we identified a small molecule compound Q34 that can efficiently block cellular entry of the pseudotyped SARS-CoV-2 into human ACE2-expressing HEK293T cells, and inhibit the infection of the authentic SARS-CoV-2 in human ACE2-expressing HEK293T cells, human iPSC-derived neurons and astrocytes, and human lung Calu-3 cells. Importantly, the safety profile of the compound is favorable. There is no obvious toxicity observed in uninfected cells treated with the compound. Thus, this compound holds great potential as both prophylactics and therapeutics for COVID-19 and future pandemics by blocking the entry of SARS-CoV-2 and related viruses into human cells.

Dynamic-Fusion-Based Federated Learning for COVID-19 Detection.

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients' privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients' training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.

Mobility in China, 2020: a tale of four phases.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.