NAC family is a class of transcription factors that have been typically found in plant with various functions. This type of genes plays a key regulatory role in secondary plant growth, cell division, senescence, especially in the hormone and signaling pathways. ANAC092 was reported to function in relation to lateral root development and senescence. The promoter and in situ hybridization analyses showed that ANAC092 was expressed temporally in the process of anther development. The gene was expressed in tapetum at stages 8-11 of anther development and reached the highest amount at stages 9-10, which was overlapped with the expression period of AMS (Aborted microspores). In this study, we constructed ANAC092 over-expression system, and identified homozygous transgenic lines. Compared with the wild type, the number of pollen grains in the transgenic line was decreased significantly, but the length of pollen grain was increased. qRT-PCR analysis showed that the expression of genes related to pollen development, e.g., SPL, EMS1, DYT1, and AMS was in-creased in the over-expression plants. Bioinformatics results showed that ANAC092 promoter sequence possessed seven AMS binding sites. All the results showed that ANAC092 is possibly located in the downstream of AMS and plays an im-portant role in the process of pollen development.

Background: The cochineal cactus (Opuntia cochenillifera), notable for its substantial agricultural and industrial applications, predominantly undergoes clonal reproduction, which presents significant challenges in breeding and germplasm innovation. Recent developments in mitochondrial genome engineering offer promising avenues for introducing heritable mutations, potentially facilitating selective sexual reproduction through the creation of cytoplasmic male sterile genotypes. However, the lack of comprehensive mitochondrial genome information for Opuntia species hinders these efforts. Here, we intended to sequence and characterize its mitochondrial genome to maximize the potential of its genomes for evolutionary studies, molecular breeding, and molecular marker developments.Results: We sequenced the total DNA of the O. cochenillifera using DNBSEQ and Nanopore platforms. The mitochondrial genome was then assembled using a hybrid assembly strategy using Unicycler software. We found that the mitochondrial genome of O. cochenillifera has a length of 1,156,235 bp, a GC content of 43.06%, and contains 54 unique protein-coding genes and 346 simple repeats. Comparative genomic analysis revealed 48 homologous fragments shared between mitochondrial and chloroplast genomes, with a total length of 47,935 bp. Additionally, the comparison of mitochondrial genomes from four Cactaceae species highlighted their dynamic nature and frequent mitogenomic reorganizations.Conclusion: Our study provides a new perspective on the evolution of the organelle genome and its potential application in genetic breeding. These findings offer valuable insights into the mitochondrial genetics of Cactaceae, potentially facilitating future research and breeding programs aimed at enhancing the genetic diversity and adaptability of O. cochenillifera by leveraging its unique mitochondrial genome characteristics.

To explore the taxon‐dependent contribution of dispersal limitation versus environmental filters to bryophyte assemblages. We investigated bryophytes and six environmental variables on 168 islands in the Thousand Island Lake,China. We compared the observed beta diversity with the expected values based on six null models (EE, EF, FE, FF, PE, and PF), detected the partial correlation of beta diversity with geographical distances. We quantified the contributions of spatial versus environmental variables and island isolation per se to species composition (SC) using variance partitioning. We modeled the species‐area relationships (SARs) for bryophytes and the other eight biotas. To explore the taxon‐dependent effects of spatial versus environmental filters on bryophytes, 16 taxa including five categories (total bryophytes, total mosses, liverworts, acrocarpous, and pleurocarpous mosses) and 11 species‐richest families were included in the analyses. The observed beta diversity values were significantly different from the predicted values for all 16 taxa. For all five categories, the observed partial correlations between beta diversity and geographical distance after controlling environmental effects were not only positive, but also significantly different from the predicted values based on the null models. Spatial eigenvectors are more important in shaping SC than environmental variables for all 16 taxa except Brachytheciaceae and Anomodontaceae. Spatial eigenvectors contributed more to SC variation in liverworts than in mosses and in pleurocarpous mosses than in acrocarpous mosses. The effects of island isolation on SC were significant for all five categories, highly varied at the family level. The z values of the SARs for the five bryophyte categories were all larger than those of the other eight biotas. In subtropical fragmented forests, dispersal limitation exerted significant, taxon‐dependent effects on bryophyte assemblages. It was dispersal limitation rather than environmental filtering that predominantly regulated the SC patterns of bryophytes.

Bryophytes, including liverworts, mosses, and hornworts, play an irreplaceable role in soil moisture retention, erosion prevention, and pollution monitoring. The precise identification of bryophyte species enhances our understanding and utilization of their ecological functions. However, their complex morphology and structural symmetry make identification difficult. Although deep learning improves classification efficiency, challenges remain due to limited datasets and the inadequate adaptation of existing methods to multi-scale features, causing poor performance in fine-grained multi-classification. Thus, we propose MOSSNet, a lightweight neural network for bryophyte feature detection. It has a four-stage architecture that efficiently extracts multi-scale features using a modular design with symmetry consideration in feature representation. At the input stage, the Convolutional Patch Embedding (CPE) module captures representative features through a two-layer convolutional structure. In each subsequent stage, Dual-Branch Multi-scale (DBMS) modules are employed, with one branch utilizing convolutional operations and the other utilizing the Dilated Convolution Enhanced Attention (DCEA) module for multi-scale feature fusion. The DBMS module extracts fine-grained and coarse-grained features by a weighted fusion of the outputs from two branches. Evaluating MOSSNet on the self-constructed dataset BryophyteFine reveals a Top-1 accuracy of 99.02% in classifying 26 bryophyte species, 7.13% higher than the best existing model, while using only 1.58 M parameters, 0.07 G FLOPs.