Rice landraces, including Asian rice (Oryza sativa L.) and African rice (Oryza glaberrima Steud.), provide important genetic resources for rice breeding to address challenges related to food security. Due to climate change and farm destruction, rice landraces require urgent conservation action. Recognition of the geographical distributions of rice landraces will promote further collecting efforts. Here we modelled the potential distributions of eight rice landrace subgroups using 8351 occurrence records combined with environmental predictors with Maximum Entropy (MaxEnt) algorithm. The results showed they were predicted in eight sub-regions, including the Indus, Ganges, Meghna, Mekong, Yangtze, Pearl, Niger, and Senegal river basins. We then further revealed the changes in suitable areas of rice landraces under future climate change. Suitable areas showed an upward trend in most of study areas, while sub-regions of North and Central China and West Coast of West Africa displayed an unsuitable trend indicating rice landraces are more likely to disappear from fields in these areas. The above changes were mainly determined by changing global temperature and precipitation. Those increasingly unsuitable areas should receive high priority in further collections. Overall, these results provide valuable references for further collecting efforts of rice landraces, while shedding light on global biodiversity conservation.
The contributions of crop germplasm resources to food security depend on their conservation and accessibility for use. The automated warehouse has begun to be applied to the ex situ preservation of crop germplasm resources in the crop genebank. Identifying the proper storage scheme for potentially hundreds of thousands of seeds is a primary task that faces the crop genebank manager during the design of a new automated crop genebank. There are mainly three areas to focus on, hardware and software, seeds storage assignment policy and seeds labelling technology. This paper aims to propose automated crop genebank storage schemes for two kinds of crop genebank (the long-term crop genebank and the middle-term crop genebank), which supports managers in determining the technologies that can be applied in the automated crop genebank. Firstly, the selection of hardware and software should be based on the functional orientation of the long-term crop genebank and the middle-term crop genebank. Secondly, for the seed storage assignment policy, the sequential storage assignment is designed for the long-term genebank while the cache storage assignment is developed for the middle-term genebank. Finally, a QR code labelling technology based on image recognition is designed for both the long-term crop genebank and the middle-term crop genebank.
Background Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying cellular heterogeneity of rice root. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.Results To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using an external dataset, with 98.01% accuracy and 95.45% recall. With the power of the interpretability provided by NRTPredictor, our model recognizes 110 important marker genes, partially involved in the phenylpropanoid biosynthesis, that domain knowledge does not consider. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrative bulk RNA-seq analysis we revealed aberrant expression of Epidermis and Cortex cell subpopulations in flooding, Pi stress and salt stress.Conclusion Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi stress and salt stress. Based on the proposed model, a free webserver has been established, which is available at http://bio.germplasmai.com.
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