Human respiratory syncytial viruses (RSVs) are classified into two major groups (A and B) based on antigenic differences in the G glycoprotein. To investigate circulating characteristics and phylodynamic history of RSV, we analyzed the genetic variability and evolutionary pattern of RSVs from 1977 to 2019 in this study. The results revealed that there was no recombination event of intergroup. Single nucleotide polymorphisms (SNPs) were observed through the genome with the highest occurrence rate in the G gene. Five and six sites in G protein of RSV-A and RSV-B, respectively, were further identified with a strong positive selection. The mean evolutionary rates for RSV-A and -B were estimated to be 1.48 × 10–3 and 1.92 × 10–3 nucleotide substitutions/site/year, respectively. The Bayesian skyline plot showed a constant population size of RSV-A and a sharp expansion of population size of RSV-B since 2005, and an obvious decrease 5 years later, then became stable again. The total population size of RSVs showed a similar tendency to that of RSV-B. Time-scaled phylogeny suggested a temporal specificity of the RSV-genotypes. Monitoring nucleotide changes and analyzing evolution pattern for RSVs could give valuable insights for vaccine and therapy strategies against RSV infection.
Objective Bone and muscle, two major tissue types of musculoskeletal system, have strong genetic determination. Abnormality in bone and/or muscle may cause musculoskeletal diseases such as osteoporosis and sarcopenia. Bone size phenotypes (BSPs), such as hip bone size (HBS), appendicular bone size (ABS), are genetically correlated with body lean mass (mainly muscle mass). However, the specific genes shared by these phenotypes are largely unknown. In this study, we aimed to identify the specific genes with pleiotropic effects on BSPs and appendicular lean mass (ALM). Methods We performed a bivariate genome-wide association study (GWAS) by analyzing ~690,000 SNPs in 1,627 unrelated Han Chinese adults (802 males and 825 females) followed by a replication study in 2,286 unrelated US Caucasians (558 males and 1728 females). Results We identified 14 interesting single nucleotide polymorphisms (SNPs) that may contribute to variation of both BSPs and ALM, with p values <10−6 in discovery stage. Among them, the association of three SNPs (rs2507838, rs7116722, and rs11826261) in/near GLYAT (glycine-N-acyltransferase) gene was replicated in US Caucasians, with p values ranging from 1.89×10−3 to 3.71×10−4 for ALM-ABS, from 5.14×10−3 to 1.11×10−2 for ALM-HBS, respectively. Meta-analyses yielded stronger association signals for rs2507838, rs7116722, and rs11826261, with pooled p values of 1.68×10−8, 7.94×10−8, 6.80×10−8 for ALB-ABS and 1.22×10−4, 9.85×10−5, 3.96×10−4 for ALM-HBS, respectively. Haplotype allele ATA based on these three SNPs were also associated with ALM-HBS and ALM-ABS in both discovery and replication samples. Interestingly, GLYAT was previously found to be essential to glucose metabolism and energy metabolism, suggesting the gene’s dual role in both bone development and muscle growth. Conclusions Our findings, together with the prior biological evidence, suggest the importance of GLYAT gene in co-regulation of bone phenotypes and body lean mass.
Irregularities in railway tracks are a key factor influencing the safety of trains. In this paper, rail track is considered to consist of consecutive track maintenance units whose individual defect states can be quantified in terms of a track quality index. A Markov stochastic process approach is used to evaluate the deterioration of a maintenance unit. A hazard model is formulated using the heterogeneity of the maintenance units, and a matrix of the Markov transition probabilities is constructed. The parameters of the developed models are estimated via a maximum log-likelihood function. The prediction model is validated with track irregularity data measured using track geometry cars.
In the process of train operation, the interaction between train wheels and railway track underneath leads to changes in track geometry. Changes in track geometry result in track irregularity. For managing the mean value of track irregularity, Railway Maintenance Department in China uses track quality index (TQI) to quantify track irregularity of a unit track section. TQI is used for planning and scheduling railway track maintenance and guiding track maintenance work in China. Based on the characteristics of changes in TQI and in accordance with differential and integral principles, a novel track irregularity prediction technique has been developed in this article. This technique utilizes track waveform data generated by a track geometry car to build different track irregularity prediction models for each unit section and use different models to make predictions for TQIs of different unit sections along the railway track on each day in a future short-range period of time. In this article, the technique was applied to 25 sets of track waveforms generated for Beijing-Shanghai railway up-going track (Jing-Hu up-going track) administrated by Jinan Railway Bureau and errors in predicted TQI were analysed in both spatial and temporal dimensions. The error analysis results show that some maintenance works, such as temping, levelling, and aligning have significant influence on TQI predictions. The methods for weakening the influence of such track maintenance and the usage of TQI predictions were also discussed.
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