Unraveling the supply-service relationship between high-speed railway and conventional railway: A temporal perspective

With the rapid development of high-speed railway (HSR), many scholars have studied the competition between HSR and aviation or inter-city coach, but few studies have discussed the supply-service relationship within the railway system. This paper explores the competition and cooperation between HSR and conventional railway (CR) at the city-pair level of 39 core cities in China from the temporal perspective. Comprehensive considering the departure time and arrival time between city pairs as the representation of train service quality, we find that the proportion of superior quality service of high-speed train (HST) is far higher than that of conventional train (CT). However, the time slots representing the competition degree show that CR with fewer superior quality trains is easier to be replaced than HSR. The supply-service relationships of the railway system indicate that HSR has become the main transportation mode between core cities, and the CR, as an auxiliary transportation, shows a certain complementary effect in the temporal perspective. Spatially, HSR and CR services are more likely to generate temporal competition on the mainline, but temporal complementarity between city pairs on non-arterial lines. This study can provide inspiration for regional spatial planning by better understanding the operation strategy of railway passenger transport system

Design of Wide-Spectrum Inhibitors Targeting Coronavirus Main Proteases

The genus Coronavirus contains about 25 species of coronaviruses (CoVs), which are important pathogens causing highly prevalent diseases and often severe or fatal in humans and animals. No licensed specific drugs are available to prevent their infection. Different host receptors for cellular entry, poorly conserved structural proteins (antigens), and the high mutation and recombination rates of CoVs pose a significant problem in the development of wide-spectrum anti-CoV drugs and vaccines. CoV main proteases (M(pro)s), which are key enzymes in viral gene expression and replication, were revealed to share a highly conservative substrate-recognition pocket by comparison of four crystal structures and a homology model representing all three genetic clusters of the genus Coronavirus. This conclusion was further supported by enzyme activity assays. Mechanism-based irreversible inhibitors were designed, based on this conserved structural region, and a uniform inhibition mechanism was elucidated from the structures of M(pro)-inhibitor complexes from severe acute respiratory syndrome-CoV and porcine transmissible gastroenteritis virus. A structure-assisted optimization program has yielded compounds with fast in vitro inactivation of multiple CoV M(pro)s, potent antiviral activity, and extremely low cellular toxicity in cell-based assays. Further modification could rapidly lead to the discovery of a single agent with clinical potential against existing and possible future emerging CoV-related diseases

Expression of IMP1 Enhances Production of Murine Leukemia Virus Vector by Facilitating Viral Genomic RNA Packaging

Murine leukemia virus (MLV)-based retroviral vector is widely used for gene transfer. Efficient packaging of the genomic RNA is critical for production of high-titer virus. Here, we report that expression of the insulin-like growth factor II mRNA binding protein 1 (IMP1) enhanced the production of infectious MLV vector. Overexpression of IMP1 increased the stability of viral genomic RNA in virus producer cells and packaging of the RNA into progeny virus in a dose-dependent manner. Downregulation of IMP1 in virus producer cells resulted in reduced production of the retroviral vector. These results indicate that IMP1 plays a role in regulating the packaging of MLV genomic RNA and can be used for improving production of retroviral vectors

MicroRNAs recruit eIF4E2 to repress translation of target mRNAs

Abstract MicroRNAs (miRNAs) recruit the RNA-induced silencing complex (RISC) to repress the translation of target mRNAs. While the 5′ 7-methylguanosine cap of target mRNAs has been well known to be important for miRNA repression, the underlying mechanism is not clear. Here we show that TNRC6A interacts with eIF4E2, a homologue of eIF4E that can bind to the cap but cannot interact with eIF4G to initiate translation, to inhibit the translation of target mRNAs. Downregulation of eIF4E2 relieved miRNA repression of reporter expression. Moreover, eIF4E2 downregulation increased the protein levels of endogenous IMP1, PTEN and PDCD4, whose expression are repressed by endogenous miRNAs. We further provide evidence showing that miRNA enhances eIF4E2 association with the target mRNA. We propose that miRNAs recruit eIF4E2 to compete with eIF4E to repress mRNA translation

Large-Scale Estimation of Hourly Surface Air Temperature Based on Observations from the FY-4A Geostationary Satellite

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth’s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China’s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets