Genetic comparison of transmissible gastroenteritis coronaviruses

Transmissible gastroenteritis virus (TGEV) is a porcine coronavirus that threatens animal health and remains elusive despite years of research efforts. The systematical analysis of all available full-length genomes of TGEVs (a total of 43) and porcine respiratory coronaviruses PRCVs (a total of 7) showed that TGEVs fell into two independent evolutionary phylogenetic clades, GI and GII. Viruses circulating in China (until 2021) clustered with the traditional or attenuated vaccine strains within the same evolutionary clades (GI). In contrast, viruses latterly isolated in the USA fell into GII clade. The viruses circulating in China have a lower similarity with that isolated latterly in the USA all through the viral genome. In addition, at least four potential genomic recombination events were identified, three of which occurred in GI clade and one in GII clade. TGEVs circulating in China are distinct from the viruses latterly isolated in the USA at either genomic nucleotide or antigenic levels. Genomic recombination serves as a factor driving the expansion of TGEV genomic diversity

Multiple potential recombination events among Newcastle disease virus genomes in China between 1946 and 2020

IntroductionNewcastle Disease Virus (NDV) is a highly adaptable virus with large genetic diversity that has been widely studied for its oncolytic activities and potential as a vector vaccine. This study investigated the molecular characteristics of 517 complete NDV strains collected from 26 provinces across China between 1946-2020.MethodsHerein, phylogenetic, phylogeographic network, recombination, and amino acid variability analyses were performed to reveal the evolutionary characteristics of NDV in China.Results and discussionsPhylogenetic analysis revealed the existence of two major groups: GI, which comprises a single genotype Ib, and GII group encompassing eight genotypes (I, II, III, VI. VII. VIII, IX and XII). The Ib genotype is found to dominate China (34%), particularly South and East China, followed by VII (24%) and VI (22%). NDV strains from the two identified groups exhibited great dissimilarities at the nucleotide level of phosphoprotein (P), matrix protein (M), fusion protein (F), and haemagglutinin-neuraminidase (HN) genes. Consistently, the phylogeographic network analysis revealed two main Network Clusters linked to a possible ancestral node from Hunan (strain MH289846.1). Importantly, we identified 34 potential recombination events that involved mostly strains from VII and Ib genotypes. A recombinant of genotype XII isolated in 2019 seems to emerge newly in Southern China. Further, the vaccine strains are found to be highly involved in potential recombination. Therefore, since the influence of recombination on NDV virulence cannot be predicted, this report’s findings need to be considered for the security of NDV oncolytic application and the safety of NDV live attenuated vaccines

A Multigrid Multilevel Monte Carlo Method for Stokes–Darcy Model with Random Hydraulic Conductivity and Beavers–Joseph Condition

A multigrid multilevel Monte Carlo (MGMLMC) method is developed for the stochastic Stokes–Darcy interface model with random hydraulic conductivity both in the porous media domain and on the interface. Three interface conditions with randomness are considered on the interface between Stokes and Darcy equations, especially the Beavers–Joesph interface condition with random hydraulic conductivity. Because the randomness through the interface affects the flow in the Stokes domain, we investigate the coupled stochastic Stokes–Darcy model to improve the fidelity. Under suitable assumptions on the random coefficient, we prove the existence and uniqueness of the weak solution of the variational form. To construct the numerical method, we first adopt the Monte Carlo (MC) method and finite element method, for the discretization in the probability space and physical space, respectively. In order to improve the efficiency of the classical single-level Monte Carlo (SLMC) method, we adopt the multilevel Monte Carlo (MLMC) method to dramatically reduce the computational cost in the probability space. A strategy is developed to calculate the number of samples needed in MLMC method for the stochastic Stokes–Darcy model. In order to accomplish the strategy for MLMC method, we also present a practical method to determine the variance convergence rate for the stochastic Stokes–Darcy model with Beavers–Joseph interface condition. Furthermore, MLMC method naturally provides the hierarchical grids and sufficient information on these grids for multigrid (MG) method, which can in turn improve the efficiency of MLMC method. In order to fully make use of the dynamical interaction between these two methods, we propose a multigrid multilevel Monte Carlo (MGMLMC) method with finite element discretization for more efficiently solving the stochastic model, while additional attention is paid to the interface and the random Beavers–Joesph interface condition. The computational cost of the proposed MGMLMC method is rigorously analyzed and compared with the SLMC method. Numerical examples are provided to verify and illustrate the proposed method and the theoretical conclusions

Genome-Wide Association Study and Transcriptome Differential Expression Analysis of the Feather Rate in Shouguang Chickens

The feather rate phenotype in chicks, including early-feathering and late-feathering phenotypes, are widely used as a sexing system in the poultry industry. The objective of this study was to obtain candidate genes associated with the feather rate in Shouguang chickens. In the present study, we collected 56 blood samples and 12 hair follicle samples of flight feathers from female Shouguang chickens. Then we identified the chromosome region associated with the feather rate by genome-wide association analysis (GWAS). We also performed RNA sequencing and analyzed differentially expressed genes between the early-feathering and late-feathering phenotypes using HISAT2, StringTie, and DESeq2. We identified a genomic region of 10.0–13.0 Mb of chromosome Z, which is statistically associated with the feather rate of Shouguang chickens at one-day old. After RNA sequencing analysis, 342 differentially expressed known genes between the early-feathering (EF) and late-feathering (LF) phenotypes were screened out, which were involved in epithelial cell differentiation, intermediate filament organization, protein serine kinase activity, peptidyl-serine phosphorylation, retinoic acid binding, and so on. The sperm flagellar 2 gene (SPEF2) and prolactin receptor (PRLR) gene were the only two overlapping genes between the results of GWAS and differential expression analysis, which implies that SPEF2 and PRLR are possible candidate genes for the formation of the chicken feathering phenotype in the present study. Our findings help to elucidate the molecular mechanism of the feather rate in chicks.</p

Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. In other words, at each time, the attacker has the freedom to choose an arbitrary set of no more that $p$ sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error subject to $2p$-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observer for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by solving an optimization problem to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we optimize the detector threshold to ensure that the benign sensors do not trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system