Nucleotide and dinucleotide preference of segmented viruses are shaped more by segment: In case study of tomato spotted wilt virus.

Several studies have showed that the nucleotide and dinucleotide composition of viruses possibly follows their host species or protein coding region. Nevertheless, the influence of viral segment on viral nucleotide and dinucleotide composition is still unknown. Here, we explored through tomato spotted wilt virus (TSWV), a segmented virus that seriously threatens the production of tomatoes all over the world. Through nucleotide composition analysis, we found the same over-representation of A across all viral segments at the first and second codon position, but it exhibited distinct in segments at the third codon position. Interestingly, the protein coding regions which encoded by the same or different segments exhibit obvious distinct nucleotide preference. Then, we found that the dinucleotides UpG and CpU were overrepresented and the dinucleotides UpA, CpG and GpU were underrepresented, not only in the complete genomic sequences, but also in different segments, protein coding regions and host species. Notably, 100% of the data investigated here were predicted to the correct viral segment and protein coding region, despite the fact that only 67% of the data analyzed here were predicted to the correct viral host species. In conclusion, in case study of TSWV, nucleotide composition and dinucleotide preference of segment viruses are more strongly dependent on segment and protein coding region than on host species. This research provides a novel perspective on the molecular evolutionary mechanisms of TSWV and provides reference for future research on genetic diversity of segmented viruses.

Brain age prediction across the human lifespan using multimodal MRI data.

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.

Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest.

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

An Evolutionary Perspective of Codon Usage Pattern, Dinucleotide Composition and Codon Pair Bias in Prunus Necrotic Ringspot Virus.

Prunus necrotic ringspot virus (PNRSV) is a significant virus of ornamental plants and fruit trees. It is essential to study this virus due to its impact on the horticultural industry. Several studies on PNRSV diversity and phytosanitary detection technology were reported, but the content on the codon usage bias (CUB), dinucleotide preference and codon pair bias (CPB) of PNRSV is still uncertain. We performed comprehensive analyses on a dataset consisting of 359 coat protein (CP) gene sequences in PNRSV to examine the characteristics of CUB, dinucleotide composition, and CPB. The CUB analysis of PNRSV CP sequences showed that it was not only affected by natural selection, but also affected by mutations, and natural selection played a more significant role compared to mutations as the driving force. The dinucleotide composition analysis showed an over-expression of the CpC/GpA dinucleotides and an under-expression of the UpA/GpC dinucleotides. The dinucleotide composition of the PNRSV CP gene showed a weak association with the viral lineages and hosts, but a strong association with viral codon positions. Furthermore, the CPB of PNRSV CP gene is low and is related to dinucleotide preference and codon usage patterns. This research provides reference for future research on PNRSV genetic diversity and gene evolution mechanism.

Host Plants Shape the Codon Usage Pattern of Turnip Mosaic Virus.

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.

The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks.

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 default-mode 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.