Background: Cytoplasmic male sterility (CMS) is a complex phenomenon of plant sterility that can produce non-functional pollen. It is caused by mutation, rearrangement or recombination in the mitochondrial genome. So far, the systematic structural characteristics of the changes in the mitochondrial genome from the maintainer lines to the CMS lines have not been reported in tobacco. Results: The mitochondrial genomes of the flower buds from both CMS lines and maintainer lines of two Nicotiana tabacum cultivars (YY85, sYY85, ZY90, and sZY90) were sequenced using the PacBio and Illumina Hiseq technology, and several findings were produced by comparative analysis based on the de novo sequencing. (1) The genomes of the CMS lines were larger, and the different areas were mostly non-coding regions. (2) A large number of rearrangement regions were detected in the CMS lines, with many translocation regions. (3) Thirteen gene clusters were shared by the four mitochondrial genomes, among which two of the gene clusters, nad2-sdh3 and nad6-rps4, were far from each other in the CMS lines. (4) Thirty-three protein-coding genes were conserved in four mitochondrial genomes. However, nad3 was detected one additional copy in the maintainer lines, and sequence differences were revealed in the four candidate genes (atp6, cox2, nad2, and sdh3). Importantly, the evolutionary tree based on the four genes could be used to distinguish the CMS lines and the maintainer lines well for the sequenced mitochondrial genomes of the tobacco. (5) Sixteen CMSspecific open reading frames (ORFs) were found, three of which (orf91, orf115b, and orf100) were previously reported. (6) The differences in intensity of the protein-protein (PPI) interaction in ATP6 were further verified using the yeast two-hybrid analysis. Conclusion: Although the majority of the sequences, genes and gene clusters were shared by the mitochondrial genomes of the maintainer and the CMS lines in

For precision medicine, there is a need to identify genes that accurately distinguish the physiological state or response to a particular therapy, but this can be challenging. Many methods of analyzing differential expression have been established and applied to this problem, such as t-test, edgeR, and DEseq2. A common feature of these methods is their focus on a linear relationship (differential expression) between gene expression and phenotype. However, they may overlook nonlinear relationships due to various factors, such as the degree of disease progression, sex, age, ethnicity, and environmental factors. Maximal information coefficient (MIC) was proposed to capture a wide range of associations of two variables in both linear and nonlinear relationships. However, with MIC it is difficult to highlight genes with nonlinear expression patterns as the genes giving the most strongly supported hits are linearly expressed, especially for noisy data. It is thus important to also efficiently identify nonlinearly expressed genes in order to unravel the molecular basis of disease and to reveal new therapeutic targets. We propose a novel nonlinearity measure called normalized differential correlation (NDC) to efficiently highlight nonlinearly expressed genes in transcriptome datasets. Validation using six real-world cancer datasets revealed that the NDC method could highlight nonlinearly expressed genes that could not be highlighted by t-test, MIC, edgeR, and DEseq2, although MIC could capture nonlinear correlations. The classification accuracy indicated that analysis of these genes could adequately distinguish cancer and paracarcinoma tissue samples. Furthermore, the results of biological interpretation of the identified genes suggested that some of them were involved in key functional pathways associated with cancer progression and metastasis. All of this evidence suggests that these nonlinearly expressed genes may play a central role in regulating cancer progression.

Since the decision trees (DTs) have an advantage over "black-box" models, such as neural nets or support vector machines, in terms of comprehensibility, such that it might merit improvement for further optimization. The node splitting measures and pruning methods are primary among the techniques that can improve the generalization abilities of DTs. Here, we introduced the unequal interval optimization for node splitting, as well as the local chi-square test for tree pruning. This new method was named an adaptive multibranch decision tree (CMDT). 11 benchmark data sets with different scales were chosen from UCI Machine Learning Repository and coupled with 12 classifiers to evaluate the CMDT algorithm. The results showed that CMDT can be more reliable than the twelve comparative approaches, especially for imbalanced datasets. We also discussed the performance metrics and the weighted decision-making table in unbalanced data sets. The CMDT algorithm can be found here: https://github.com/chenyuan0510/CMDT. INDEX TERMS decision tree, node splitting, Chi-MIC, CMDT, pruning methods.

Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

This paper presents the analytical and numerical investigation on the global synchronization and anti-synchronization for a class of drive-response systems of fractional-order complex-valued gene regulatory networks with time-varying delays (DFGRNs). In our design, two kinds of adaptive feedback controllers are used to synchronize and anti-synchronize the proposed drive-response systems, and some sufficient conditions on the global asymptotical synchronization and anti-synchronization are given with the methods of the fractional Lyapunov-like functions and the fractional-order inequalities. In the numerical simulations, two minimum "estimated time", T 1 and T 2 , are computed to achieve the synchronization and anti-synchronization. We find that T 1 and T 2 increase with the decreasing of the fractional order of DFGRNs. INDEX TERMS Complex-valued, feedback controller, fractional-order, gene regulatory networks (GRNs), synchronization and anti-synchronization, time-varying delays.