Search Results (2)

Crop Wild Relatives (CWRs) form a comprehensive gene pool that can answer the queries related to plant domestication, speciation, and ecological adaptation. The genus ‘Oryza’ comprises about 27 species, of which two are cultivated, while the remaining are wild. Here, we have attempted to understand the conservation and diversification of the genes encoding Cystathionine β-synthase (CBS) domain-containing proteins (CDCPs) in domesticated and CWRs of rice. Few members of CDCPs were previously identified to be stress-responsive and associated with multiple stress tolerance in rice. Through genome-wide analysis of eleven rice genomes, we identified a total of 36 genes encoding CDCPs in O. longistaminata, 38 in O. glaberrima, 39 each in O. rufipogon, O. glumaepatula, O. brachyantha, O. punctata, and O. sativa subsp. japonica, 40 each in O. barthii and O. meridionalis, 41 in O. nivara, and 42 in O. sativa subsp. indica. Gene duplication analysis as well as non-synonymous and synonymous substitutions in the duplicated gene pairs indicated that this family is shaped majorly by the negative or purifying selection pressure through the long-term evolution process. We identified the presence of two additional hetero-domains, namely TerCH and CoatomerE (specifically in O. sativa subsp. indica), which were not reported previously in plant CDCPs. The in silico expression analysis revealed some of the members to be responsive to various abiotic stresses. Furthermore, the qRT-PCR based analysis identified some members to be highly inducive specifically in salt-tolerant genotype in response to salinity. The cis-regulatory element analysis predicted the presence of numerous stress as well as a few phytohormone-responsive elements in their promoter region. The data presented in this study would be helpful in the characterization of these CDCPs from rice, particularly in relation to abiotic stress tolerance. Full article

To allow the intelligent detection of correct answers in the rice-related question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, we propose an answer selection model with dynamic attention and multi-strategy matching (DAMM). According to the characteristics of the rice-related dataset, the twelve-layer Chinese Bert pre-training model was employed to vectorize the text data and was compared with Word2vec, GloVe, and TF-IDF (Term Frequency–Inverse Document Frequency) methods. It was concluded that Bert could effectively solve the agricultural text’s high dimensionality and sparsity problems. As well as the problem of polysemy having different meanings in different contexts, dynamic attention with two different filtering strategies was used in the attention layer to effectively remove the sentence’s noise. The sentence representation of question-and-answer sentences was obtained. Secondly, two matching strategies (Full matching and Attentive matching) were introduced in the matching layer to complete the interaction between sentence vectors. Thirdly, a bi-directional gated recurrent unit (BiGRU) network spliced the sentence vectors obtained from the matching layer. Finally, a classifier was employed to calculate the similarity of splicing vectors, and the semantic correlation between question-and-answer sentences was acquired. The experimental results showed that DAMM had the best performance in the rice-related answer selection dataset compared with the other six answer selection models, of which MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) of DAMM gained 85.7% and 88.9%, respectively. Compared with the other six kinds of answer selection models, we present a new state-of-the-art method with the rice-related answer selection dataset. Full article