Chrysosplenium, a perennial herb in the family Saxifragaceae, prefers to grow in low light and moist environments and is divided into two sections of Alternifolia and Oppositifolia based on phyllotaxy. Although there has been some progress in the phylogeny of Chrysosplenium over the years, the phylogenetic position of some species is still controversial. In this study, we assembled chloroplast genomes (cp genomes) of 34 Chrysosplenium species and performed comparative genomic and phylogenetic analyses in combination with other cp genomes of previously known Chrysosplenium species, for a total of 44 Chrysosplenium species. The comparative analyses revealed that cp genomes of Chrysosplenium species were more conserved in terms of genome structure, gene content and arrangement, SSRs, and codon preference, but differ in genome size and SC/IR boundaries. Phylogenetic analysis showed that cp genomes effectively improved the phylogenetic support and resolution of Chrysosplenium species and strongly supported Chrysosplenium species as a monophyletic taxon and divided into three branches. The results also showed that the sections of Alternifolia and Oppositifolia were not monophyletic with each other, and that C. microspermum was not clustered with other Chrysosplenium species with alternate leaves, but with C. sedakowii into separate branches. In addition, we identified 10 mutational hotspot regions that could serve as potential DNA barcodes for Chrysosplenium species identification. In contrast to Peltoboykinia, the clpP and ycf2 genes of Chrysosplenium were subjected to positive selection and had multiple significant positive selection sites. We further detected a significant positive selection site on the petG gene between the two sections of Chrysosplenium. These evolutionary characteristics may be related to the growth environment of Chrysosplenium species. This study enriches the cp genomes of Chrysosplenium species and provides a reference for future studies on its evolution and origin.

The genus Carthamus Linnaeus, which belongs to the tribe Cardueae in the Asteraceae family, originated in the Mediterranean region and consists of approximately 20 species worldwide. Understanding the phylogeny of the Carthamus is crucial for the cultivation of C. tinctorius. Although chloroplast genomes are widely used for species identification and evolutionary studies, there have been limited investigations on the chloroplast genomes of Carthamus species. In this study, we assembled the chloroplast genomes of C. persicus, C. tinctorius × C. persicus, and C. lanatus and combined them with the five chloroplast genomes of C. tinctorius for comparative genomic analysis. The sizes of the chloroplast genomes of C. lanatus, C. persicus, and C. tinctorius × C. persicus were 152,602 bp, 153,177 bp, and 153,177 bp, respectively. Comparative analysis showed that the chloroplast genome structures of the four Carthamus species were highly conserved. Additionally, the phylogenomic analysis demonstrated that the plastid genome and angiosperms353 dataset significantly improved the phylogenetic support of Carthamus species. This analysis supported Carthamus as a monophyletic taxon and its internal division into the sect. Carthamus and sect. Atractylis. The Carthamus was closely related to Carduncellus, Femeniasia, Phonus, and Centaurea. In conclusion, this study not only expands our understanding of the cp genomes of Carthamus species but also provides support for more comprehensive phylogenetic studies of Carthamus.

Farmland changes have a profound impact on agricultural ecosystems and global food security, making the timely and accurate detection of these changes crucial. Remote sensing image change detection provides an effective tool for monitoring farmland dynamics, but existing methods often struggle with high-resolution images due to complex scenes and insufficient multi-scale information capture, particularly in terms of missed detections. Missed detections can lead to underestimating land changes, which affects key areas such as resource allocation, agricultural decision-making, and environmental management. Traditional CNN-based models are limited in extracting global contextual information. To address this, we propose a CNN-Transformer-based Multi-Scale Attention Siamese Network (MT-SiamNet), with a focus on reducing missed detections. The model first extracts multi-scale local features using a CNN, then aggregates global contextual information through a Transformer module, and incorporates an attention mechanism to increase focus on key change areas, thereby effectively reducing missed detections. Experimental results demonstrate that MT-SiamNet achieves superior performance across multiple change detection datasets. Specifically, our method achieves an F1 score of 65.48% on the HRSCD dataset and 75.02% on the CLCD dataset, significantly reducing missed detections and improving the reliability of farmland change detection, thereby providing strong support for agricultural decision-making and environmental management.