Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of massive taxi trip data of Shanghai, we built spatially-embedded networks to model the intra-city spatial interactions and introduced network science methods into the issue. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long-and short-distance trips, we reveal a two-level hierarchical polycentric city structure of Shanghai. Further explorations on sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential in local traffic. By incorporating the land use of centers from the travel pattern perspective, we investigate sub-region formation and center-local places interaction patterns. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in applying urban and transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than other structures, such as administrative divisions.

During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

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Q fever is a worldwide zoonosis caused by Coxiella burnetii (Cb). From January 2018 to November 2019, plasma samples from 2,382 patients with acute fever of unknown cause at a hospital in Zhuhai city of China were tested using metagenomic next-generation sequencing (mNGS). Of those tested, 138 patients (5.8%) were diagnosed with Q fever based on the presence of Cb genomic DNA detected by mNGS. Among these, 78 cases (56.5%) presented from Nov 2018 to Mar 2019, suggesting an outbreak of Q fever. 55 cases with detailed clinical information that occurred during the outbreak period were used for further analysis. The vast majority of plasma samples from those Cb-mNGS-positive patients were positive in a Cb-specific quantitative polymerase chain reaction (n = 38) and/or indirect immunofluorescence assay (n = 26). Mobile phone tracing data was used to define the area of infection during the outbreak. This suggested the probable infection source was Cb-infected goats and cattle at the only official authorized slaughterhouse in Zhuhai city. Phylogenic analysis based on genomic sequences indicated Cb strains identified in the patients, goat and cattle were formed a single branch, most closely related to the genomic group of Cb dominated by strains isolated from goats. Our study demonstrates Q fever was epidemic in 2018–2019 in Zhuhai city, and this is the first confirmed epidemic of Q fever in a contemporary city in China.

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Understanding the dynamics of lung microbiota in tuberculosis patients, especially those who cannot be confirmed bacteriologically in clinical practice, is imperative for accurate diagnosis and effective treatment. This study aims to characterize the distinct lung microbial features between bacteriologically confirmed and negative tuberculosis patients to understand the influence of microbiota on tuberculosis patients. We collected specimens of bronchoalveolar lavage fluid from 123 tuberculosis patients. Samples were subjected to metagenomic next-generation sequencing to reveal the lung microbial signatures. By combining conventional bacterial detection and metagenomic sequencing, 101/123 (82%) tuberculosis patients were bacteriologically confirmed. In addition to Mycobacterium tuberculosis, Staphylococcus aureus, Kluyveromyces lactis, and Pyricularia pennisetigena were also enriched in the bacteriological confirmation group. In contrast, Haemophilus parainfluenzae was enriched in the bacteriologically negative group. Besides, microbial interaction exhibits a different state between bacteriologically confirmed and negative tuberculosis patients. Mycobacterium tuberculosis was confirmed correlated with clinical characteristics such as albumin and chest cavities. Our study comprehensively demonstrates the correlation between unique features of lung microbial dynamics and the clinical characteristics of tuberculosis patients, suggesting the importance of studying the pulmonary microbiome in tuberculosis disease and providing new insights for future precision diagnosis and treatment.