A Spatiotemporal Bidirectional Attention-Based Ride-Hailing Demand Prediction Model: A Case Study in Beijing During COVID-19

The COVID-19 pandemic has severely affected urban transport patterns, including the way residents travel. It is of great significance to predict the demand of urban ride-hailing for residents’ healthy travel, rational platform operation, and traffic control during the epidemic period. In this paper, we propose a deep learning model, called MOS-BiAtten, based on multi-head spatial attention mechanism and bidirectional attention mechanism for ride-hailing demand prediction. The model follows the encoder-decoder framework with a multi-output strategy for multi-steps prediction. The pre-predicted result and the historical demand data are extracted as two aspects of bidirectional attention flow, so as to further explore the complicated spatiotemporal correlations between the historical, present and future information. The proposed model is evaluated on the real-world dataset during COVID-19 in Beijing, and the experimental results demonstrate that MOS-BiAtten achieves a better performance compared with the state-of-art methods. Meanwhile, another dataset is used to verify the generalization performance of the model.

SARS-CoV-2 non-structural protein 6 triggers NLRP3-dependent pyroptosis by targeting ATP6AP1.

A recent mutation analysis suggested that Non-Structural Protein 6 (NSP6) of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a key determinant of the viral pathogenicity. Here, by transcriptome analysis, we demonstrated that the inflammasome-related NOD-like receptor signaling was activated in SARS-CoV-2-infected lung epithelial cells and Coronavirus Disease 2019 (COVID-19) patients' lung tissues. The induction of inflammasomes/pyroptosis in patients with severe COVID-19 was confirmed by serological markers. Overexpression of NSP6 triggered NLRP3/ASC-dependent caspase-1 activation, interleukin-1ß/18 maturation, and pyroptosis of lung epithelial cells. Upstream, NSP6 impaired lysosome acidification to inhibit autophagic flux, whose restoration by 1α,25-dihydroxyvitamin D3, metformin or polydatin abrogated NSP6-induced pyroptosis. NSP6 directly interacted with ATP6AP1, a vacuolar ATPase proton pump component, and inhibited its cleavage-mediated activation. L37F NSP6 variant, which was associated with asymptomatic COVID-19, exhibited reduced binding to ATP6AP1 and weakened ability to impair lysosome acidification to induce pyroptosis. Consistently, infection of cultured lung epithelial cells with live SARS-CoV-2 resulted in autophagic flux stagnation, inflammasome activation, and pyroptosis. Overall, this work supports that NSP6 of SARS-CoV-2 could induce inflammatory cell death in lung epithelial cells, through which pharmacological rectification of autophagic flux might be therapeutically exploited.

A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality.

The COVID-19 epidemic has had a great adverse impact on the world, having taken a heavy toll, killing hundreds of thousands of people. In order to help the world better combat COVID-19 and reduce its death toll, this study focuses on the COVID-19 mortality. First, using the multiple stepwise regression analysis method, the factors from eight aspects (economy, society, climate etc.) that may affect the mortality rates of COVID-19 in various countries is examined. In addition, a two-layer nested heterogeneous ensemble learning-based prediction method that combines linear regression (LR), support vector machine (SVM), and extreme learning machine (ELM) is developed to predict the development trends of COVID-19 mortality in various countries. Based on data from 79 countries, the experiment proves that age structure (proportion of the population over 70 years old) and medical resources (number of beds) are the main factors affecting the mortality of COVID-19 in each country. In addition, it is found that the number of nucleic acid tests and climatic factors are correlated with COVID-19 mortality. At the same time, when predicting COVID-19 mortality, the proposed heterogeneous ensemble learning-based prediction method shows better prediction ability than state-of-the-art machine learning methods such as LR, SVM, ELM, random forest (RF), long short-term memory (LSTM) etc.

Lysosome activation in peripheral blood mononuclear cells and prognostic significance of circulating LC3B in COVID-19.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, causing significant mortality. There is a mechanistic relationship between intracellular coronavirus replication and deregulated autophagosome-lysosome system. We performed transcriptome analysis of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and identified the aberrant upregulation of genes in the lysosome pathway. We further determined the capability of two circulating markers, namely microtubule-associated proteins 1A/1B light chain 3B (LC3B) and (p62/SQSTM1) p62, both of which depend on lysosome for degradation, in predicting the emergence of moderate-to-severe disease in COVID-19 patients requiring hospitalization for supplemental oxygen therapy. Logistic regression analyses showed that LC3B was associated with moderate-to-severe COVID-19, independent of age, sex and clinical risk score. A decrease in LC3B concentration <5.5 ng/ml increased the risk of oxygen and ventilatory requirement (adjusted odds ratio: 4.6; 95% CI: 1.1-22.0; P = 0.04). Serum concentrations of p62 in the moderate-to-severe group were significantly lower in patients aged 50 or below. In conclusion, lysosome function is deregulated in PBMCs isolated from COVID-19 patients, and the related biomarker LC3B may serve as a novel tool for stratifying patients with moderate-to-severe COVID-19 from those with asymptomatic or mild disease. COVID-19 patients with a decrease in LC3B concentration <5.5 ng/ml will require early hospital admission for supplemental oxygen therapy and other respiratory support.

Bioinformatic analyses hinted at augmented T helper 17 cell differentiation and cytokine response as the central mechanism of COVID‐19–associated Guillain‐Barré syndrome

ObjectivesGuillain‐Barré syndrome (GBS) results from autoimmune attack on the peripheral nerves, causing sensory, motor and autonomic abnormalities. Emerging evidence suggests that there might be an association between COVID‐19 and GBS. Nevertheless, the underlying pathophysiological mechanism remains unclear.Materials and MethodsWe performed bioinformatic analyses to delineate the potential genetic crosstalk between COVID‐19 and GBS.ResultsCOVID‐19 and GBS were associated with a similar subset of immune/inflammation regulatory genes, including TNF, CSF2, IL2RA, IL1B, IL4, IL6 and IL10. Protein‐protein interaction network analysis revealed that the combined gene set showed an increased connectivity as compared to COVID‐19 or GBS alone, particularly the potentiated interactions with CD86, IL23A, IL27, ISG20, PTGS2, HLA‐DRB1, HLA‐DQB1 and ITGAM, and these genes are related to Th17 cell differentiation. Transcriptome analysis of peripheral blood mononuclear cells from patients with COVID‐19 and GBS further demonstrated the activation of interleukin‐17 signalling in both conditions.ConclusionsAugmented Th17 cell differentiation and cytokine response was identified in both COVID‐19 and GBS. PBMC transcriptome analysis also suggested the pivotal involvement of Th17 signalling pathway. In conclusion, our data suggested aberrant Th17 cell differentiation as a possible mechanism by which COVID‐19 can increase the risk of GBS.