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Preterm birth (PTB), accompanied by low birth weight (LBW) or not, is a syndrome with tremendous risk factors and long-term health consequences for children. In recent decades, overwhelming studies have shown that periodontitis contributes to prematurity and LBW. This study was conducted to determine the link between maternal periodontitis and the pathogenesis of PTB and/or LBW through a rat infection model induced by Porphyromonas gingivalis, an important periodontopathic bacterium. The murine model was established by surgically ligating the left mandibular first molars and inoculating with P. gingivalis, and then all female rats initiated mating 6 weeks post infection. The gestational day and birth weight were recorded, and blood, amniotic fluid, and placental specimens were collected. Rats with a PTB and LBW newborns were observed in the P. gingivalis-infected group. Additionally, P. gingivalis infection significantly increased the maternal serum levels of interferon-γ and interleukin-1β, whereas no significant difference in the cytokine response was observed in the amniotic fluid. Moreover, with the translocation of P. gingivalis to placentas, remarkable changes in gestational tissues were found, followed by significantly enhanced expression of Toll-like receptor 2 (TLR2) as well as Fas and Fas ligand (FasL). These results support the concept that severe cases of periodontitis caused by P. gingivalis infection may be indicative of rats being more susceptible to PTB/LBW, probably through the activation of the TLR2 and Fas/FasL pathways within the placental tissues. This study gave us new insight into how maternal periodontopathogens might be linked to placental damage and premature pathogenesis.

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Urban road transport and land use (RTLU) jointly promote economic development by concentrating labor, material, and capital. This paper presents an integrated RTLU efficiency analysis that explores the degree of coordination between these two systems to provide guidance for future adaptations necessary for sustainable urban development. Both a super efficiency Data Envelopment Analysis model and window analysis were used to spatiotemporally evaluate RTLU efficiency from 2012 to 2016 in 14 cities of Hunan province, central China. The Malmquist index was decomposed into technical efficiency and technology change to reveal reasons for changes in RTLU efficiency. These evaluation results show regional disparities in efficiency across Hunan province, with western cities being the least efficient. Eight cities showed an increasing trend in RTLU efficiency while Yueyang exhibited a decreasing trend. In 13 of 14 regions, productivity improved every year. At the same time, five regions had a decline in technical efficiency even though technical progress increased in all regions. Our analysis shows that greater investment in road transport and urban construction are not enough to ensure sustainable urban growth. Policy must instead promote the full use of current resources according to local conditions to meet local, regional, and national development goals.

During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high rendering delay, and poor visual effects. Research has shown that the development of distributed computing frameworks provides a feasible solution for big spatio-temporal data management and visualization. Accordingly, to address these challenges, this paper adopts a proprietary pre-processing visualization scheme and designs and implements a highly scalable distributed visual analysis framework, especially targeted at massive point-type datasets. Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid. The proposed MAP model can support the simultaneous hierarchical aggregation of time, space, and attributes, and also later transformation of the derived aggregates into discrete key-value pairs for scalable storage and efficient retrieval. Using the generated MAP datasets, we develop an open-source distributed visualization framework (MAP-Vis). In MAP-Vis, a high-performance Spark cluster is used as a parallel preprocessing platform, while distributed HBase is used as the massive storage for the generated MAP data. The client of MAP-Vis provides a variety of correlated visualization views, including heat map, time series, and attribute histogram. Four open datasets, with record numbers ranging from the millions to the tens of billions, are chosen for system demonstration and performance evaluation. The experimental results demonstrate that MAP-Vis can achieve millisecond-level query response and support efficient interactive visualization under different queries on the space, time, and attribute dimensions.

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Driving analysis of urban expansion (DAUE) is usually implemented to identify the driving factors and their corresponding driving effects/mechanisms for the expansion processes of urban land, aiming to provide scientific guidance for urban planning and management. Based on a thorough analysis and summarization of the development process and quantitative models, four major limitations in existing DAUE studies have been uncovered: (1) the interactions in hierarchical urban systems have not been fully explored; (2) the employed data cannot fully depict urban dynamic through finer social perspectives; (3) the employed models cannot deal with high-level feature correlations; and (4) the simulation and analysis models are still not intrinsically integrated. Four future directions are thus proposed: (1) to pay attention to the hierarchical characteristics of urban systems and conduct multi-scale research on the complex interactions within them to capture dynamic features; (2) to leverage remote sensing data so as to obtain diverse urban expansion data and assimilate multi-source spatiotemporal big data to supplement novel socio-economic driving factors; (3) to integrate with interpretable data-driven machine learning techniques to bolster the performance and reliability of DAUE models; and (4) to construct mechanism-coupled urban simulation to achieve a complementary enhancement and facilitate theory development and testing for urban land systems.