Planting rice in saline–alkali land can effectively improve saline–alkali soil and increase grain yield, but traditional identification methods for saline–alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the Python machine deep learning method was used to analyze the Raman molecular spectroscopy of rice and assist in feature attribution, in order to study a fast and efficient identification method of saline–alkali-tolerant rice varieties. A total of 156 Raman spectra of four rice varieties (two saline–alkali-tolerant rice varieties and two saline–alkali-sensitive rice varieties) were analyzed, and the wave crests were extracted by an improved signal filtering difference method and the feature information of the wave crest was automatically extracted by scipy.signal.find_peaks. Select K Best (SKB), Recursive Feature Elimination (RFE) and Select F Model (SFM) were used to select useful molecular features. Based on these feature selection methods, a Logistic Regression Model (LRM) and Random Forests Model (RFM) were established for discriminant analysis. The experimental results showed that the RFM identification model based on the RFE method reached a higher recognition rate of 89.36%. According to the identification results of RFM and the identification of feature attribution materials, amylum was the most significant substance in the identification of saline–alkali-tolerant rice varieties. Therefore, an intelligent method for the identification of saline–alkali-tolerant rice varieties based on Raman molecular spectroscopy is proposed.

Background: As key members of the plant receptor-like kinase family, rice CrRLK1Ls play diverse roles in plant growth, development, and stress responses. Although rice CrRLK1Ls have been initially characterized, our understanding of their functions remains limited. Methods: We identified OsCrRLK1L genes via Hidden Markov Model (HMM) searches against the rice genome. Subsequent analyses encompassed their physicochemical properties, chromosomal distribution, gene structure, phylogenetic relationships, conserved domains, and cis-acting elements.Salt-responsive candidates were screened using a GEO dataset, and their expression profiles were validated under salt stress using quantitative real-time PCR. Result: A total of 36 OsCrRLK1L genes, all containing both Malectin and tyrosine kinase domains, were identified in the rice genome and showed an uneven chromosomal distribution. Phylogenetic analysis classified them into three subclades, with Group II and Group III being specific to rice and Arabidopsis thaliana, respectively. Promoter analysis revealed that the promoter regions of these genes contained an abundance of cis-acting elements related to hormone and stress responses. RNA-Seq and enrichment analysis indicated that OsCrRLK1L genes exhibit tissue specificity and participate in salt stress responses. Furthermore, CrRLK1L2 and CrRLK1L10 showed tissue-specific differential expression under salt stress. Conclusions: In summary, our study lays the groundwork for future research into the biological roles of OsCrRLK1L genes during salt stress.

Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.

Background: Cold stress is a major abiotic factor limiting rice growth and yield. Elucidating the molecular mechanisms underlying cold tolerance is therefore of great significance for variety improvement. This study focused on the cold-tolerant variety QJ10 and the cold-sensitive variety DHX2, systematically comparing their physiological and transcriptomic differences under cold stress and identifying genes and modules associated with the cold stress response. Methods: After 0, 3, 5, and 7 days of treatment at 4 °C, we measured leaf MDA and Pro contents, as well as SOD and POD activities. We performed multi-time-point transcriptome sequencing using RNA-seq, and conducted data mining and validation through differential expression analysis, Mfuzz trend clustering, WGCNA co-expression network analysis, GENIE3 regulatory network prediction, and qRT-PCR. Results: Compared with DHX2, QJ10 exhibited lower MDA levels and higher levels of Pro, SOD, and POD under cold stress. Transcriptome analysis identified a total of 13,599 differentially expressed genes. Trend clustering revealed that QJ10 primarily maintained genes associated with growth, development, and basal metabolism, whereas DHX2 tended to activate defense responses. WGCNA identified the MEturquoise module, which showed significant positive correlations with both cold treatment duration and the activities of SOD and POD. Genes in this module were significantly enriched in pathways such as carbon metabolism, photosynthesis, and ion transport. Twelve key transcription factors were identified, nine of which were highly expressed at the late stage of cold stress in QJ10. GENIE3 further predicted seven key regulatory factors centered on OsNAC2, OsLBD, and OsARF19; the expression patterns of these factors were validated by qRT-PCR and were consistent with the transcriptomic results. Conclusions: This study revealed that the cold tolerance of QJ10 is associated with enhanced antioxidant capacity, upregulation of genes related to carbon metabolism, and the induced expression of specific transcription factors. The key transcription factors identified here provide candidate genes for studying the molecular mechanisms of cold tolerance in rice. However, their regulatory functions require further experimental validation.

Rice is regarded as the preferred crop for saline-alkali soil improvement by researchers. At present, the identification method for saline-alkali tolerance of rice varieties requires researcher to conduct tedious field investigations based on growth indicators. Therefore, there is an urgent need for an effective technical means to quickly and accurately identify saline-alkali tolerance of rice varieties. Study used 20 japonica rice varieties with three types of saline-alkali tolerance as test materials, by analyzing the identification mechanism of salt-alkali tolerance in Raman spectrum of japonica rice varieties, seven characteristic spectral peaks closely related to salt-alkali tolerance were identified. Various algorithms in Python are used for data standardization, baseline elimination, extraction of characteristic spectral peaks, detection of peaks characteristic information and data noise reduction. Three identification models were established to confirm the highest accuracy of CapsNets identification model, which could provide technical support and reference for breeding saline-alkali resistant japonica rice varieties.

Alkaline stress is one of the most critical limiting factors affecting high and stable rice yields. Therefore, analyzing the impact of alkaline stress on rice productivity and identifying salt- and alkali-tolerant genes are crucial for breeding rice varieties with strong stress resistance. In this study, the alkali-sensitive variety QJ74 and the alkali-tolerant variety QJ22 were used as parental lines. Under alkaline conditions (pH 9.0), 30 alkali-sensitive and 30 alkali-tolerant F₂ individuals from the segregating population were selected for BSA-Seq. Transcriptomic analysis was performed on the parental lines under the same alkaline treatment. The results showed that under alkali stress, QJ22 exhibited significantly greater plant height and chlorophyll content, but significantly lower relative electrical conductivity and malondialdehyde content than QJ74, indicating less severe cell membrane damage. Meanwhile, QJ22 showed significantly higher proline content and superoxide dismutase and peroxidase activities, together with significantly lower Na⁺ and higher K⁺ contents compared with QJ74. Overall, QJ22 outperformed QJ74 in cell membrane stability, photosynthetic pigment maintenance, antioxidant enzyme activity, and osmotic regulation, demonstrating strong alkali tolerance. Based on these findings, under alkali stress (pH 9.0), 30 alkali-sensitive and 30 alkali-tolerant F₂ individuals from the cross between the two parents were selected for BSA-Seq, and transcriptomic sequencing was performed on the parental lines under the same conditions. A combined BSA-Seq and RNA-Seq analysis identified five candidate genes (Os10g0376200, Os10g0389000, Os10g0389500, Os10g0390500 and Os10g0401000). Subsequent cis-acting element analysis, phylogenetic analysis, and qRT-PCR validation revealed that Os10g0389000 is a promising alkali stress-responsive candidate gene potentially contributing to the differential alkali tolerance between QJ74 and QJ22. This study integrated BSA-Seq and RNA-Seq to systematically identify candidate genes involved in the alkali stress response in rice, providing key genetic targets and a molecular basis for developing new alkali-tolerant varieties.