Molecular Identification and Phylogenetic Study Based on the Fusion Gene of Newcastle Disease Virus Isolated from Broiler Poultry Farms in Markazi Province, Iran.

Newcastle disease (ND) is an economically significant and extremely spreadable viral illness affecting a wide variety of avian species. ND can rapidly spread within poultry farms and result in considerable economic losses for the global poultry industry. This disease is endemic in Iran, and despite intensive vaccination efforts in the poultry industry, outbreaks of ND occur unexpectedly. This study aimed to isolate the Newcastle disease virus (NDV) from poultry farms with breathing problems in Markazi province, Iran, and investigate the evolutionary relationship and molecular characteristics of the isolates during 2017-2019. To this end, tissue samples (lung, brain, and trachea) were taken from 42 broiler farms exhibiting respiratory symptoms. The samples were inoculated into 9-11-day-old embryonated eggs, and the virus was isolated from 20 (47.6%) of the 42 farms. Subsequently, RT-PCR was used to amplify partial fusion gene sequences from the new isolates. The amplified products were sequenced and compared phylogenetically to the standard pilot dataset (125 selected sequences) generated by the NDV consortium. As determined by phylogenetic analysis, all nine isolates belonged to subgenotype VII.1.1 of genotype VII and were highly similar to isolates from other parts of Iran and China. Moreover, all isolates possessed a polybasic cleavage site motif (112RRQKRF117), characteristic of virulent strains. Furthermore, the present isolates shared a high nucleotide identity (96%) with viruses previously isolated from other provinces of Iran, as determined by BLAST searches and multiple alignments. In addition, they shared a high degree of sequence similarity but were distinct from the existing NDV vaccines. Therefore, the genetic dissimilarity between current vaccine strains and circulating NDVs must be considered in vaccination programs.

A method for estimating and characterizing explicitly nonlinear dynamic functional network connectivity in resting-state fMRI data.

The past 10 years have seen an explosion of approaches that focus on the study of time-resolved change in functional connectivity (FC). FC characterization among networks at a whole-brain level is frequently termed functional network connectivity (FNC). Time-resolved or dynamic functional network connectivity (dFNC) focuses on the estimation of transient, recurring, whole-brain patterns of FNC. While most approaches in this area have attempted to capture dynamic linear correlation, we are particularly interested in whether explicitly nonlinear relationships, above and beyond linear, are present and contain unique information. This study thus proposes an approach to assess explicitly nonlinear dynamic functional network connectivity (EN dFNC) derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information (NMI). Simulations showed the proposed method estimated explicitly nonlinearity over time, even within relatively short windows of data. We then, applied our approach on 151 schizophrenia patients, and 163 healthy controls fMRI data and found three unique, highly structured, mostly long-range, functional states that also showed significant group differences. In particular, explicitly nonlinear relationships tend to be more widespread than linear ones. Results also highlighted a state with long range connections to the visual domain, which were significantly reduced in schizophrenia. Overall, this work suggests that quantifying EN dFNC may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.