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The linearity and stationarity of fMRI time series need to be understood due to their important roles in the choice of approach for brain network analysis. In this paper, we investigated the stationarity and linearity of resting-state fMRI (rs-fMRI) time-series data from the Midnight Scan Club datasets. The degree of stationarity (DS) and the degree of non-linearity (DN) were, respectively, estimated for the time series of all gray matter voxels. The similarity and difference between the DS and DN were assessed in terms of voxels and intrinsic brain networks, including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default-mode network. The test-retest scans were utilized to quantify the reliability of DS and DN. We found that DS and DN maps had overlapping spatial distribution. Meanwhile, the probability density estimate function of DS had a long tail, and that of DN had a more normal distribution. Specifically, stronger DS was present in the somatomotor, limbic, and ventral attention networks compared to other networks, and stronger DN was found in the somatomotor, visual, limbic, ventral attention, and defaultmode networks. The percentage of overlapping voxels between DS and DN in different networks demonstrated a decreasing trend in the order default mode, ventral attention, somatomotor, frontoparietal, dorsal attention, visual, and limbic. Furthermore, the ICC values of DS were higher than those of DN. Our results suggest that different functional networks have distinct properties of non-stationarity and non-linearity owing to the complexity of rs-fMRI time series. Thus, caution should be taken when analyzing fMRI data (both resting-state and task-activation) using simplified models.

Turnip mosaic virus (TuMV), an important pathogen that causes mosaic diseases in vegetable crops worldwide, belongs to the genus Potyvirus of the family Potyviridae. Previously, the areas of genetic variation, population structure, timescale, and migration of TuMV have been well studied. However, the codon usage pattern and host adaptation analysis of TuMV is unclear. Here, compositional bias and codon usage of TuMV were performed using 184 non-recombinant sequences. We found a relatively stable change existed in genomic composition and a slightly lower codon usage choice displayed in TuMV protein-coding sequences. Statistical analysis presented that the codon usage patterns of TuMV protein-coding sequences were mainly affected by natural selection and mutation pressure, and natural selection was the key influencing factor. The codon adaptation index (CAI) and relative codon deoptimization index (RCDI) revealed that TuMV genes were strongly adapted to Brassica oleracea from the present data. Similarity index (SiD) analysis also indicated that B. oleracea is potentially the preferred host of TuMV. Our study provides the first insights for assessing the codon usage bias of TuMV based on complete genomes and will provide better advice for future research on TuMV origins and evolution patterns.

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