A Wide Extent of Inter-Strain Diversity in Virulent and Vaccine Strains of Alphaherpesviruses

Alphaherpesviruses are widespread in the human population, and include herpes simplex virus 1 (HSV-1) and 2, and varicella zoster virus (VZV). These viral pathogens cause epithelial lesions, and then infect the nervous system to cause lifelong latency, reactivation, and spread. A related veterinary herpesvirus, pseudorabies (PRV), causes similar disease in livestock that result in significant economic losses. Vaccines developed for VZV and PRV serve as useful models for the development of an HSV-1 vaccine. We present full genome sequence comparisons of the PRV vaccine strain Bartha, and two virulent PRV isolates, Kaplan and Becker. These genome sequences were determined by high-throughput sequencing and assembly, and present new insights into the attenuation of a mammalian alphaherpesvirus vaccine strain. We find many previously unknown coding differences between PRV Bartha and the virulent strains, including changes to the fusion proteins gH and gB, and over forty other viral proteins. Inter-strain variation in PRV protein sequences is much closer to levels previously observed for HSV-1 than for the highly stable VZV proteome. Almost 20% of the PRV genome contains tandem short sequence repeats (SSRs), a class of nucleic acids motifs whose length-variation has been associated with changes in DNA binding site efficiency, transcriptional regulation, and protein interactions. We find SSRs throughout the herpesvirus family, and provide the first global characterization of SSRs in viruses, both within and between strains. We find SSR length variation between different isolates of PRV and HSV-1, which may provide a new mechanism for phenotypic variation between strains. Finally, we detected a small number of polymorphic bases within each plaque-purified PRV strain, and we characterize the effect of passage and plaque-purification on these polymorphisms. These data add to growing evidence that even plaque-purified stocks of stable DNA viruses exhibit limited sequence heterogeneity, which likely seeds future strain evolution

Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

Bas, Eren/0000-0002-0263-8804; Egrioglu, Erol/0000-0003-4301-4149WOS: 000434268000024Single multiplicative neuron model and multilayer perceptron have been commonly used for time series prediction problem. Having a simple structure and features of easily applicable differentiates the single multiplicative neuron model from the multilayer perception. While, multilayer perceptron just as many other artificial neural networks are data-based methods, single multiplicative neuron model has a model structure due to it is composed of a single neuron. Multilayer perceptron can highly compliance with data by changing its architecture, though single multiplicative neuron model, in this respect, is insufficient. In this study, to overcome this problem of single multiplicative neuron model, a new model that its weights and biases are obtained by way of autoregressive equations is proposed. Since the time indexes are considered to determine weights and biases from the autoregressive models, the proposed neural network can be evaluated as a data-based model. To show the performance and capability of the proposed method, various implementations have been executed over some well-known data sets. And the obtained results demonstrate that data-based proposed method has outstanding forecasting performance