Motivation Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information. Results In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network (GNN) model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method. Availability and Implementation The Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.

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The rising mental health difficulties of the urban population in developing countries may be attributed to the high levels of air pollution. However, nationwide large-scale empirical works that examine this claim are rare. In this study, we construct a daily mental health metric using the volume of mental-health-related queries on the largest search engine in China, Baidu, to test this hypothesis. We find that air pollution causally undermines people’s mental health and that this impact becomes stronger as the duration of exposure to air pollution increases. Heterogeneity analyses reveal that men, middle-aged people and married people are more vulnerable to the impact of air pollution on mental health. More importantly, the results also demonstrate that the cumulative effects of air pollution on mental health are smaller for people living in cities with a higher gross domestic product per capita, more health resources, larger areas of green land and more sports facilities. Finally, we estimate that with a one-standard-deviation increase of fine particulate matter (26.3 μg m−3), the number of people who suffer from mental health problems in China increases by approximately 1.15 million. Our findings provide quantitative evidence for the benefits of reducing air pollution to promote mental health and well-being.

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Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.