Rice plants experience various biotic (such as insect and pest attack) and abiotic (such as drought, salt, heat, and cold etc.) stresses during the growing season, resulting in DNA damage and the subsequent losses in rice production. DNA Replication Helicase/Nuclease2 (DNA2) is known to be involved in DNA replication and repair. In animals and yeast DNA2 are well characterized because it has the abilities of both helicase and nuclease, it plays a crucial role in DNA replication in the nucleus and mitochondrial genomes. However; they are not fully examined in plants due to less focused on plants damage repair. To fill this research gap, the current study focused on the genome-wide identification and characterization of OsDNA2 genes, along with analyses of their transcriptional expression, duplication, and phylogeny in rice. Overall, 17 OsDNA2 members were reported to be found on eight different chromosomes (2, 3, 4, 6, 7, 9, 10, and 11). Among these chromosomes (Chr), Chr4 contained a maximum of six OsDNA2 genes. Based on phylogenetic analysis, the OsDNA2 gene members were clustered into three different groups. Furthermore, the conserved domains, gene structures, and cis-regulatory elements were systematically investigated. Gene duplication analysis revealed that OsDNA2_2 had an evolutionary relationship with OsDNA2_14, OsDNA2_5 with OsDNA2_6, and OsDNA2_1 with OsDNA2_8. Moreover, results showed that the conserved domain (AAA_11 superfamily) were present in the OsDNA2 genes, which belongs to the DEAD-like helicase superfamily. In addition, to understand the post-transcriptional modification of OsDNA2 genes, miRNAs were predicted, where 653 miRNAs were reported to target 17 OsDNA2 genes. The results indicated that at the maximum, OsDNA2_1 and OsDNA2_4 were targeted by 74 miRNAs each, and OsDNA2_9 was less targeted (20 miRNAs). The three-dimensional (3D) structures of 17 OsDNA2 proteins were also predicted. Expression of OsDNA2 members was also carried out under drought and salt stresses, and conclusively their induction indicated the possible involvement of OsDNA2 in DNA repair under stress when compared with the control. Further studies are recommended to confirm where this study will offer valuable basic data on the functioning of DNA2 genes in rice and other crop plants.

Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties and using their resistance genes in breeding is an effective and economical control measure. Accurate severity estimation of this disease is crucial for breeding resistant cotton varieties. However, current methods fall short, slowing the breeding process. To address these challenges, this paper introduces CVW-Etr, a high-precision method for estimating the severity of cotton verticillium wilt. CVW-Etr classifies severity into six levels (L0 to L5) based on the proportion of segmented diseased leaves to lesions. Upon integrating YOLOv8-Seg with MobileSAM, CVW-Etr demonstrates excellent performance and efficiency with limited samples in complex field conditions. It incorporates the RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, and GSegment modules to handle blurry transitions between healthy and diseased regions and variations in angle and distance during image collection, and to optimize the model’s parameter size and computational complexity. Our experimental results show that CVW-Etr effectively segments diseased leaves and lesions, achieving a mean average precision (mAP) of 92.90% and an average severity estimation accuracy of 92.92% with only 2.6M parameters and 10.1G FLOPS. Through experiments, CVW-Etr proves robust in estimating cotton verticillium wilt severity, offering valuable insights for disease-resistant cotton breeding applications.

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes.

Rice is sensitive to high temperatures at the seedling stage. In the present study, a combined analysis of transcriptome and metabolome was performed on a heat-resistant accession, DY80, from Dongxiang wild rice and a heat-sensitive variety, R974, under heat stress at the seedling stage. The results of the transcriptome and metabolome analyses were verified through qRT-PCR and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis. We found that there were 1817 and 561 differentially expressed genes (DEGs) unique in DY80 and R974 under heat stress, respectively. The elite genes for the heat stress involved in Dongxiang wild rice may include upregulated genes in the pathway of unfolded protein binding; downregulated genes in the pathways of chlorophyll biosynthetic process, and cysteine and methionine metabolism; and photosystem I, photosystem II, and unchanged genes in the pathways of the anchored component of the plasma membrane, cell wall biogenesis, and photosynthesis-antenna proteins. Moreover, a total of 301 and 28 metabolites were identified as unique in DY80 and R974 after heat treatment, respectively. Further analyses showed that malic acid, stearic acid, and L-threonine might be causal metabolites, contributing to strong heat resistance in Dongxiang wild rice. These findings provide new insights into the mechanisms of heat resistance in rice.

Arsenic (As), a toxic element, is widely distributed in soil and irrigation water. Rice (Oryza sativa L.), the staple food in Southern China, exhibits a greater propensity for As uptake compared to other crops. Arsenic pollution in paddy fields not only impairs rice growth but also poses a serious threat to food security and human health. Nevertheless, the molecular mechanism underlying the response to As toxicity has not been completely revealed until now. Transcriptome analysis represents a powerful tool for revealing the mechanisms conferring phenotype formation and is widely employed in crop breeding. Consequently, this review focuses on the recent advances in transcriptome analysis within the realm of low As breeding in rice. It particularly highlights the applications of transcriptome analysis in identifying genes responsive to As toxicity, revealing gene interaction regulatory modules and analyzing secondary metabolite biosynthesis pathways. Furthermore, the molecular mechanisms underlying rice As tolerance are updated, and the recent outcomes in low As breeding are summarized. Finally, the challenges associated with applying transcriptome analysis to low-As breeding are deliberated upon, and future research directions are envisioned, with the aim of providing references to expedite high-yield and low-arsenic breeding in rice.

Heterosis is the phenomenon in which some hybrid traits are superior to those of their parents. Most studies have analyzed the heterosis of agronomic traits of crops; however, heterosis of the panicles can improve yield and is important for crop breeding. Therefore, a systematic study of panicle heterosis is needed, especially during the reproductive stage. RNA sequencing (RNA Seq) and transcriptome analysis are suitable for further study of heterosis. Using the Illumina Nova Seq platform, the transcriptome of ZhongZheYou 10 (ZZY10), an elite rice hybrid, the maintainer line ZhongZhe B (ZZB), and the restorer line Z7-10 were analyzed at the heading date in Hangzhou, 2022. 581 million high-quality short reads were obtained by sequencing and were aligned against the Nipponbare reference genome. A total of 9000 differential expression genes were found between the hybrids and their parents (DG_HP). Of the DG_HP, 60.71% were up-regulated and 39.29% were down-regulated in the hybrid. Comparative transcriptome analysis revealed that 5235 and 3765 DG_HP were between ZZY10 and ZhongZhe B and between ZZY10 and Z7-10, respectively. This result is consistent with the transcriptome profile of ZZY10 and was similar to Z7-10. The expression patterns of DG_HP mainly exhibited over-dominance, under-dominance, and additivity. Among the DG_HP-involved GO terms, pathways such as photosynthesis, DNA integration, cell wall modification, thylakoid, and photosystem were significant. 21 DG_HP, which were involved in photosynthesis, and 17 random DG_HP were selected for qRT-PCR validation. The up-regulated PsbQ and down-regulated subunits of PSI and PSII and photosynthetic electron transport in the photosynthesis pathway were observed in our study. Extensive transcriptome data were obtained by RNA-Seq, providing a comprehensive overview of panicle transcriptomes at the heading stage in a heterotic hybrid.