A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data

To understand the complex phenomena in social space and monitor the dynamic changes in people's tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.

Exploration of the Shared Molecular Mechanisms between COVID-19 and Neurodegenerative Diseases through Bioinformatic Analysis

The COVID-19 pandemic has caused millions of deaths and remains a major public health burden worldwide. Previous studies found that a large number of COVID-19 patients and survivors developed neurological symptoms and might be at high risk of neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD). We aimed to explore the shared pathways between COVID-19, AD, and PD by using bioinformatic analysis to reveal potential mechanisms, which may explain the neurological symptoms and degeneration of brain that occur in COVID-19 patients, and to provide early intervention. In this study, gene expression datasets of the frontal cortex were employed to detect common differentially expressed genes (DEGs) of COVID-19, AD, and PD. A total of 52 common DEGs were then examined using functional annotation, protein–protein interaction (PPI) construction, candidate drug identification, and regulatory network analysis. We found that the involvement of the synaptic vesicle cycle and down-regulation of synapses were shared by these three diseases, suggesting that synaptic dysfunction might contribute to the onset and progress of neurodegenerative diseases caused by COVID-19. Five hub genes and one key module were obtained from the PPI network. Moreover, 5 drugs and 42 transcription factors (TFs) were also identified on the datasets. In conclusion, the results of our study provide new insights and directions for follow-up studies of the relationship between COVID-19 and neurodegenerative diseases. The hub genes and potential drugs we identified may provide promising treatment strategies to prevent COVID-19 patients from developing these disorders.

Exploration of the Shared Molecular Mechanisms between COVID-19 and Neurodegenerative Diseases through Bioinformatic Analysis.

The COVID-19 pandemic has caused millions of deaths and remains a major public health burden worldwide. Previous studies found that a large number of COVID-19 patients and survivors developed neurological symptoms and might be at high risk of neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD). We aimed to explore the shared pathways between COVID-19, AD, and PD by using bioinformatic analysis to reveal potential mechanisms, which may explain the neurological symptoms and degeneration of brain that occur in COVID-19 patients, and to provide early intervention. In this study, gene expression datasets of the frontal cortex were employed to detect common differentially expressed genes (DEGs) of COVID-19, AD, and PD. A total of 52 common DEGs were then examined using functional annotation, protein-protein interaction (PPI) construction, candidate drug identification, and regulatory network analysis. We found that the involvement of the synaptic vesicle cycle and down-regulation of synapses were shared by these three diseases, suggesting that synaptic dysfunction might contribute to the onset and progress of neurodegenerative diseases caused by COVID-19. Five hub genes and one key module were obtained from the PPI network. Moreover, 5 drugs and 42 transcription factors (TFs) were also identified on the datasets. In conclusion, the results of our study provide new insights and directions for follow-up studies of the relationship between COVID-19 and neurodegenerative diseases. The hub genes and potential drugs we identified may provide promising treatment strategies to prevent COVID-19 patients from developing these disorders.

Integrated bioinformatics analysis identifies shared immune changes between ischemic stroke and COVID 19.

Although COVID-19 is primarily a respiratory disease, its neurological complications, such as ischemic stroke (IS), have aroused growing concerns and reports. However, the molecular mechanisms that underlie IS and COVID-19 are not well understood. Therefore, we implemented transcriptomic analysis from eight GEO datasets consist of 1191 samples to detect common pathways and molecular biomarkers in IS and COVID-19 that help understand the linkage between them. Differentially expressed genes (DEGs) were detected for IS and COVID-19 separately for finding shared mechanisms and we found that immune-related pathways were outlined with statistical significance. JAK2, which was identified as a hub gene, was supposed to be a potential therapeutic gene targets during the immunological process of COVID-19 and IS. Besides, we found a decrease in the proportion of CD8+ T and T helper 2 cells in the peripheral circulation of both COVID and IS patients, and NCR3 expression was significantly correlated with this change. In conclusion, we demonstrated that transcriptomic analyses reported in this study could make a deeper understanding of the common mechanism and might be promising for effective therapeutic for IS and COVID-19.

China's Public Firms' Attitudes towards Environmental Protection Based on Sentiment Analysis and Random Forest Models

In this article, we investigated changes in public firms' attitudes towards environmental protection in 2018–2021 in China. We crawled the firm–investor Q&A record on the website of East Money, extracted the carbon- and environment-related corpus, and then applied the sentiment analysis method of NLP (natural language processing) to calculate the sentiment weight of each firm-level record to estimate the attitude before and after towards carbon reduction. We found that there were significant changes in firms' attitudes towards carbon reduction and environmental protection after the COVID-19 pandemic and the implementation of environment-related policies. We also found a heterogeneous effect of the attitude in different industries. In addition, we built several models to examine the relationship between a firm's carbon reduction attitude and its financial performance. We found that: A goal with consequent specific policies can raise the positive attitudes of firms toward carbon reduction topics;firms' attitudes toward ecological topics are different from industry to industry, which means that there are different needs and situations in the trend of carbon reduction from industry to industry. COVID-19 influenced firms' attitudes toward carbon reduction and environmental protection, calling back the classic dilemma or trilemma of economic growth, carbon reduction, and energy consumption or, perhaps, epidemic control today. The stock situation also influenced the attitude toward environmental protection.

Pathogenic Basis of Thromboinflammation and Endothelial Injury in COVID-19: Current Findings and Therapeutic Implications.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic with a great impact on social and economic activities, as well as public health. In most patients, the symptoms of COVID-19 are a high-grade fever and a dry cough, and spontaneously resolve within ten days. However, in severe cases, COVID-19 leads to atypical bilateral interstitial pneumonia, acute respiratory distress syndrome, and systemic thromboembolism, resulting in multiple organ failure with high mortality and morbidity. SARS-CoV-2 has immune evasion mechanisms, including inhibition of interferon signaling and suppression of T cell and B cell responses. SARS-CoV-2 infection directly and indirectly causes dysregulated immune responses, platelet hyperactivation, and endothelial dysfunction, which interact with each other and are exacerbated by cardiovascular risk factors. In this review, we summarize current knowledge on the pathogenic basis of thromboinflammation and endothelial injury in COVID-19. We highlight the distinct contributions of dysregulated immune responses, platelet hyperactivation, and endothelial dysfunction to the pathogenesis of COVID-19. In addition, we discuss potential therapeutic strategies targeting these mechanisms.