Towards an optimal bus frequency scheduling: When the waiting time matters

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products

As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial\u2013temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of \u201chigh in winter and low in summer\u201d, while the spatial distribution forms a \u201chigh in East and low in west\u201d pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a \u201cdelayed effect\u201d of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention

Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years

Abstract With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers

Deciphering of interactions between platinated DNA and HMGB1 by hydrogen/deuterium exchange mass spectrometry

A high mobility group box 1 (HMGB1) protein has been reported to recognize both 1,2-intrastrand crosslinked DNA by cisplatin (1,2-cis-Pt-DNA) and monofunctional platinated DNA using trans-[PtCl2(NH3)(thiazole)] (1-trans-PtTz-DNA). However, the molecular basis of recognition between the trans-PtTz-DNA and HMGB1 remains unclear. In the present work, we described a hydrogen/deuterium exchange mass spectrometry (HDX-MS) method in combination with docking simulation to decipher the interactions of platinated DNA with domain A of HMGB1. The global deuterium uptake results indicated that 1-trans-PtTz-DNA bound to HMGB1a slightly tighter than the 1,2-cis-Pt-DNA. The local deuterium uptake at the peptide level revealed that the helices I and II, and loop 1 of HMGB1a were involved in the interactions with both platinated DNA adducts. However, docking simulation disclosed different H-bonding networks and distinct DNA-backbone orientations in the two Pt-DNA-HMGB1a complexes. Moreover, the Phe37 residue of HMGB1a was shown to play a key role in the recognition between HMGB1a and the platinated DNAs. In the cis-Pt-DNA-HMGB1a complex, the phenyl ring of Phe37 intercalates into a hydrophobic notch created by the two platinated guanines, while in the trans-PtTz-DNA-HMGB1a complex the phenyl ring appears to intercalate into a hydrophobic crevice formed by the platinated guanine and the opposite adenine in the complementary strand, forming a penta-layer π–π stacking associated with the adjacent thymine and the thiazole ligand. This work demonstrates that HDX-MS associated with docking simulation is a powerful tool to elucidate the interactions between platinated DNAs and proteins

Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?

Molecular conformation generation (MCG) is a fundamental and important problem in drug discovery. Many traditional methods have been developed to solve the MCG problem, such as systematic searching, model-building, random searching, distance geometry, molecular dynamics, Monte Carlo methods, etc. However, they have some limitations depending on the molecular structures. Recently, there are plenty of deep learning based MCG methods, which claim they largely outperform the traditional methods. However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks. In particular, our design algorithm is simply the clustering of the RDKIT-generated conformations. We hope our findings can help the community to revise the deep learning methods for MCG. The code of the proposed algorithm could be found at https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c