MADS-box genes encode a large family of transcription factors that play significant roles in plant growth and development. Bamboo is an important non-timber forest product worldwide, but previous studies on the moso bamboo (Phyllostachys edulis) MADS-box gene family were not accurate nor sufficiently detailed. Here, a complete genome-wide identification and characterization of the MADS-box genes in moso bamboo was conducted. There was an unusual lack of type-I MADS-box genes in the bamboo genome database ( http://202.127.18.221/bamboo/index.php ), and some of the PeMADS sequences are fragmented and/or inaccurate. We performed several bioinformatics techniques to obtain more precise sequences using transcriptome assembly. In total, 42 MADS-box genes, including six new type-I MADS-box genes, were identified in bamboo, and their structures, phylogenetic relationships, predicted conserved motifs and promoter cis-elements were systematically investigated. An expression analysis of the bamboo MADS-box genes in floral organs and leaves revealed that several key members are involved in bamboo inflorescence development, like their orthologous genes in Oryza. The ectopic overexpression of one MADS-box gene, PeMADS5, in Arabidopsis triggered an earlier flowering time and the development of an aberrant flower phenotype, suggesting that PeMADS5 acts as a floral activator and is involved in bamboo flowering. We produced the most comprehensive information on MADS-box genes in moso bamboo. Additionally, a critical PeMADS gene (PeMADS5) responsible for the transition from vegetative to reproductive growth was identified and shown to be related to bamboo floral development.

Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of Brassica napus and one of Brassica campestris L. The correlation coefficients between manual measurement and machine vision measurement of length and width were calculated using statistical methods. The length regression coefficient of both methods was 0.991, and the width regression coefficient was 0.989. In conclusion, for the first time, we utilized deep learning techniques to identify the characteristics of rapeseed pods while concurrently establishing a dataset for rapeseed pods. Our suggested approaches were successful in segmenting and counting rapeseed pods precisely. Our approach offers breeders an effective strategy for digitally analyzing phenotypes and automating the identification and screening process, not only in rapeseed germplasm resources but also in leguminous plants, like soybeans that possess pods.

Accurately quantifying flora and their respective anatomical structures within natural ecosystems is paramount for both botanical breeders and agricultural cultivators. For breeders, precise plant enumeration during the flowering phase is instrumental in discriminating genotypes exhibiting heightened flowering frequencies, while for growers, such data inform potential crop rotation strategies. Moreover, the quantification of specific plant components, such as flowers, can offer prognostic insights into the potential yield variances among different genotypes, thereby facilitating informed decisions pertaining to production levels. The overarching aim of the present investigation is to explore the capabilities of a neural network termed GhP2-YOLO, predicated on advanced deep learning techniques and multi-target tracking algorithms, specifically tailored for the enumeration of rapeseed flower buds and blossoms from recorded video frames. Building upon the foundation of the renowned object detection model YOLO v8, this network integrates a specialized P2 detection head and the Ghost module to augment the model’s capacity for detecting diminutive targets with lower resolutions. This modification not only renders the model more adept at target identification but also renders it more lightweight and less computationally intensive. The optimal iteration of GhP2-YOLOm demonstrated exceptional accuracy in quantifying rapeseed flower samples, showcasing an impressive mean average precision at 50% intersection over union metric surpassing 95%. Leveraging the virtues of StrongSORT, the subsequent tracking of rapeseed flower buds and blossom patterns within the video dataset was adeptly realized. By selecting 20 video segments for comparative analysis between manual and automated counts of rapeseed flowers, buds, and the overall target count, a robust correlation was evidenced, with R-squared coefficients measuring 0.9719, 0.986, and 0.9753, respectively. Conclusively, a user-friendly “Rapeseed flower detection” system was developed utilizing a GUI and PyQt5 interface, facilitating the visualization of rapeseed flowers and buds. This system holds promising utility in field surveillance apparatus, enabling agriculturalists to monitor the developmental progress of rapeseed flowers in real time. This innovative study introduces automated tracking and tallying methodologies within video footage, positioning deep convolutional neural networks and multi-target tracking protocols as invaluable assets in the realms of botanical research and agricultural administration.

Cotton is a valuable cash crop in many countries. Cotton fiber is a trichome that develops from a single epidermal cell and serves as an excellent model for understanding cell differentiation and other life processes. Alternative splicing (AS) of genes is a common post-transcriptional regulatory process in plants that is essential for plant growth and development. The process of AS during cotton fiber formation, on the other hand, is mainly unknown. A substantial number of multi-exon genes were discovered to be alternatively spliced during cotton fiber formation in this study, accounting for 23.31% of the total number of genes in Gossypium hirsutum. Retention intron (RI) is not necessarily the most common AS type, indicating that AS genes and processes during fiber development are very temporal and tissue-specific. When compared to fiber samples, AS is more prevalent at the fiber initiation stages and in the ovule, indicating that development stages and tissues use different AS strategies. Genes involved in fiber development have gone through stage-specific AS, demonstrating that AS regulates cotton fiber development. Furthermore, AS can be regulated by trans-regulation elements such as splicing factor and cis-regulation elements such as gene length, exon numbers, and GC content, particularly at exon–intron junction sites. Our findings also suggest that increased DNA methylation may aid in the efficiency of AS, and that gene body methylation is key in AS control. Finally, our research will provide useful information about the roles of AS during the cotton fiber development process.

To improve our understanding of the mechanism of maize seed germination under deep sowing, transcriptome sequencing and physiological metabolism analyses were performed using B73 embryos separated from ungerminated seeds (UG) or seeds germinated for 2 d at a depth of 2 cm (normal sowing, NS) or 20 cm (deep sowing, DS). Gene ontology (GO) analysis indicated that “response to oxidative stress” and “monolayer-surrounded lipid storage body” were the most significant GO terms in up- and down-regulated differentially expressed genes (DEGs) of DS. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that “phenylpropanoid biosynthesis” and “starch and sucrose metabolism” were critical processes in maize seed germination under deep-sowing conditions. Consistent with DEGs, the activities of superoxide dismutase, catalase, peroxidases and α-amylase, as well as the contents of gibberellin 4, indole acetic acid, zeatin and abscisic acid were significantly increased, while the jasmonic-acid level was dramatically reduced under deep-sowing stress. The expressions of six candidate genes were more significantly upregulated in B73 (deep-sowing-tolerant) than in Mo17 (deep-sowing-sensitive) at 20 cm sowing depth. These findings enrich our knowledge of the key biochemical pathways and genes regulating maize seed germination under deep-sowing conditions, which may help in the breeding of varieties tolerant to deep sowing.

Dynamic remodeling of the actin cytoskeleton plays a central role in the elongation of cotton fibers, which are the most important natural fibers in the global textile industry. Here, a high-resolution mapping approach combined with comparative sequencing and a transgenic method revealed that a G65V substitution in the cotton actin Gh_D04G0865 (GhACT17D in the wild-type) is responsible for the Gossypium hirsutum Ligon lintless-1 (Li₁) mutant (GhACT17DM). In the mutant GhACT17DM from Li₁ plant, Gly65 is substituted with valine on the lip of the nucleotide-binding domain of GhACT17D, which probably affects the polymerization of F-actin. Over-expression of GhACT17DM, but not GhACT17D, driven by either a CaMV35 promoter or a fiber-specific promoter in cotton produced a Li₁-like phenotype. Compared with the wild-type control, actin filaments in Li₁ fibers showed higher growth and shrinkage rates, decreased filament skewness and parallelness, and increased filament density. Taken together, our results indicate that the incorporation of GhACT17DM during actin polymerization disrupts the establishment and dynamics of the actin cytoskeleton, resulting in defective fiber elongation and the overall dwarf and twisted phenotype of the Li₁ mutant.