Poliovirus (PV) 2CATPase is the most studied 2C protein in the Picornaviridae family. It is involved in RNA replication, encapsidation and uncoating and many inhibitors have been found that target PV 2CATPase. Despite numerous investigations to characterize its functions, a high-resolution structure of PV 2C has not yet been determined. We report here the crystal structure of a soluble fragment of PV 2CATPase to 2.55Å, containing an ATPase domain, a zinc finger and a C-terminal helical domain but missing the N-terminal domain. The ATPase domain shares the common structural features with EV71 2C and other Superfamily 3 helicases. The C-terminal cysteine-rich motif folds into a CCCC type zinc finger in which four cysteine ligands and several auxiliary residues assist in zinc binding. By comparing with the known zinc finger fold groups, we found the zinc finger of 2C proteins belong to a new fold group, which we denote the “Enterovirus 2C-like” group. The C-terminus of PV 2CATPase forms an amphipathic helix that occupies a hydrophobic pocket located on an adjacent PV 2CATPase in the crystal lattice. The C-terminus mediated PV 2C-2C interaction promotes self-oligomerization, most likely hexamerization, which is fundamental to the ATPase activity of 2C. The zinc finger is the most structurally diverse feature in 2C proteins. Available structural and virological data suggest that the zinc finger of 2C might confer the specificity of interaction with other proteins. We built a hexameric ring model of PV 2CATPase and visualized the previously identified functional motifs and drug-resistant sites, thus providing a structure framework for antiviral drug development.
A cosmological preferred direction was reported from the type Ia supernovae (SNe Ia) data in recent years. We use the Union2.1 data to give a simple classification of such studies for the first time. Because the maximum anisotropic direction is independent of isotropic dark energy models, we adopt two cosmological models (ΛCDM, wCDM) for the hemisphere comparison analysis and ΛCDM model for dipole fit approach. In hemisphere comparison method, the matter density and the equation of state of dark energy are adopted as the diagnostic qualities in the ΛCDM model and wCDM model, respectively. In dipole fit approach, we fit the fluctuation of distance modulus. We find that there is a null signal for the hemisphere comparison method, while a preferred direction (b = −14.3 • ± 10.1 • , l = 307.1 • ± 16.2 • ) for the dipole fit method. This result indicates that the dipole fit is more sensitive than the hemisphere comparison method.
Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed to eliminate the redundant noises from the original streamflow series. Secondly, the partial autocorrelation function (PACF) values are explored to identify the inputs for the artificial neural network (ANN) models. Thirdly, the weights and biases of the ANN architecture are tuned and optimized by the multi-verse optimizer (MVO) algorithm. Finally, the simulated streamflow is obtained using the well-trained MVO-ANN model. The proposed hybrid model has been applied to annual streamflow observations from four hydrological stations in the upper reaches of the Yangtze River, China. Parallel experiments using non-denoising models, the back propagation neural network (BPNN) and the ANN optimized by the particle swarm optimization algorithm (PSO-ANN) have been designed and conducted to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can capture the nonlinear characteristics of the streamflow time series and thus provides more accurate forecasting results.
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