Assembly and comparative analysis of the initial complete mitochondrial genome of Primulina hunanensis (Gesneriaceae): a cave-dwelling endangered plant.

BACKGROUND: Primulina hunanensis, a troglobitic plant within the Primulina genus of Gesneriaceae family, exhibits robust resilience to arid conditions and holds great horticultural potential as an ornamental plant. The work of chloroplast genome (cpDNA) has been recently accomplished, however, the mitochondrial genome (mtDNA) that is crucial for plant evolution has not been reported. RESULTS: In this study, we sequenced and assembled the P. hunanensis complete mtDNA, and elucidated its evolutionary and phylogenetic relationships. The assembled mtDNA spans 575,242 bp with 43.54% GC content, encompassing 60 genes, including 37 protein-coding genes (PCGs), 20 tRNA genes, and 3 rRNA genes. Notably, high number of repetitive sequences in the mtDNA and substantial sequence translocation from chloroplasts to mitochondria were observed. To determine the evolutionary and taxonomic positioning of P. hunanensis, a phylogenetic tree was constructed using mitochondrial PCGs from P. hunanensis and 32 other taxa. Furthermore, an exploration of PCGs relative synonymous codon usage, identification of RNA editing events, and an investigation of collinearity with closely related species were conducted. CONCLUSIONS: This study reports the initial assembly and annotation of P. hunanensis mtDNA, contributing to the limited mtDNA repository for Gesneriaceae plants and advancing our understanding of their evolution for improved utilization and conservation.

Fortunella venosa (Champ. ex Benth.) C. C. Huang and F. hindsii (Champ. ex Benth.) Swingle as Independent Species: Evidence From Morphology and Molecular Systematics and Taxonomic Revision of Fortunella (Rutaceae).

Recently, the systematic status of Fortunella Swingle and its taxonomy has attracted much attention. Flora of China incorporates Fortunella into Citrus Linn. and treats all species of the traditional Fortunella as one species, namely Citrus japonica (Thunb.) Swingle. Furthermore, F. venosa (Champ. ex Benth.) C. C. Huang and F. hindsii (Champ. ex Benth.) Swingle are currently considered as synonyms of C. japonica. In this paper, morphological, palynological, and phylogenetic analyses were used to systematically explore the taxonomic status of traditional Fortunella. The key morphological features that differed among the Fortunella species were the leaf and the petiole hence could be key in its taxonomic classification of the species. Additionally, pollen morphological analysis based on the pollen size, germination grooves, polar, and equatorial axes also supported the separation of the species. The results of the phylogenetic analysis showed that each of the three species clustered separately, hence strongly supporting the conclusion of independent species. In addition, the phylogenetic analysis showed that the two genera clustered closely together hence our results support the incorporation of Fortunella into Citrus. Based on the above, this article has revised the classification of the traditional Fortunella and determined that this genus has three species, namely; F. venosa, F. hindsii, and F. japonica. F. venosa and F. hindsii are placed in the Citrus as separate species, and their species names still use the previous specific epithet. The revised scientific names of the new combinations of F. venosa and F. hindsii are as follows: Citrus venosa (Champ. ex Benth.) K. M. Liu, X. Z. Cai, and G. W. Hu, comb. nov. and Citrus hindsii (Champ. ex Benth.) K. M. Liu, G. W. Hu, and X. Z. Cai, comb. nov. F. venosa is the original species of Fortunella, F. venosa and F. hindsii are both listed as the second-class key protected wild plants in China. Therefore, the establishment of the taxonomic status of F. venosa and F. hindsii not only deepens our understanding, importance, and the complexity of the systematic classification of Fortunella, but is also significant for global biodiversity conservation, genetic resources for breeding purposes, and population genetics.

Comparative and Phylogenetic Analysis Based on the Chloroplast Genome of Coleanthus subtilis (Tratt.) Seidel, a Protected Rare Species of Monotypic Genus.

Coleanthus subtilis (Tratt.) Seidel (Poaceae) is an ephemeral grass from the monotypic genus Coleanthus Seidl, which grows on wet muddy areas such as fishponds or reservoirs. As a rare species with strict habitat requirements, it is protected at international and national levels. In this study, we sequenced its whole chloroplast genome for the first time using the next-generation sequencing (NGS) technology on the Illumina platform, and performed a comparative and phylogenetic analysis with the related species in Poaceae. The complete chloroplast genome of C. subtilis is 135,915 bp in length, with a quadripartite structure having two 21,529 bp inverted repeat regions (IRs) dividing the entire circular genome into a large single copy region (LSC) of 80,100 bp and a small single copy region (SSC) of 12,757 bp. The overall GC content is 38.3%, while the GC contents in LSC, SSC, and IR regions are 36.3%, 32.4%, and 43.9%, respectively. A total of 129 genes were annotated in the chloroplast genome, including 83 protein-coding genes, 38 tRNA genes, and 8 rRNA genes. The accD gene and the introns of both clpP and rpoC1 genes were missing. In addition, the ycf1, ycf2, ycf15, and ycf68 were pseudogenes. Although the chloroplast genome structure of C. subtilis was found to be conserved and stable in general, 26 SSRs and 13 highly variable loci were detected, these regions have the potential to be developed as important molecular markers for the subfamily Pooideae. Phylogenetic analysis with species in Poaceae indicated that Coleanthus and Phippsia were sister groups, and provided new insights into the relationship between Coleanthus, Zingeria, and Colpodium. This study presents the initial chloroplast genome report of C. subtilis, which provides an essential data reference for further research on its origin.

Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm.

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.