Time-Course Transcriptomic Analysis Reveals Molecular Insights into the Inflorescence and Flower Development of Cardiocrinum Giganteum
Plants（2024）
Beijing Forestry Univ
Abstract
Cardiocrinum giganteum is an endemic species of east Asia which is famous for its showy inflorescence and medicinal bulbs. Its inflorescence is a determinate raceme and the flowers bloom synchronously. Morphological observation and time-course transcriptomic analysis were combined to study the process of inflorescence and flower development of C. giganteum. The results show that the autonomic pathway, GA pathway, and the vernalization pathway are involved in the flower formation pathway of C. giganteum. A varied ABCDE flowering model was deduced from the main development process. Moreover, it was found that the flowers in different parts of the raceme in C. giganteum gradually synchronized during development, which is highly important for both evolution and ecology. The results obtained in this work improve our understanding of the process and mechanism of inflorescence and flower development and could be useful for the flowering period regulation and breeding of C. giganteum.
MoreTranslated text
Key words
Cardiocrinum giganteum,transcriptome,inflorescence,flower development,synchronization
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined