Trihelix transcription factors play a role in plant growth, development and various stress responses. Here, we identified 41 trihelix family genes in the rice genome. These OsMSLs (Myb/SANT-LIKE) were located on twelve chromosomes. Synteny analysis indicated only six duplicated gene pairs in the rice trihelix family. Phylogenetic analysis of these OsMSLs and the trihelix genes from other species divided them into five clusters. OsMSLs from different groups significantly diverged in terms of gene structure and conserved functional domains. However, all OsMSLs contained the same five cis-elements. Some of these were responsive to light and dehydration stress. All OsMSLs expressed in four tissues and six developmental stages of rice but with different expression patterns. Quantitative real-time PCR analysis revealed that the OsMSLs responded to abiotic stresses including drought and high salt stress and stress signal molecule including ABA (abscisic acid), hydrogen peroxide. OsMSL39 were simultaneously expressed under all treatments, while OsMSL28 showed high expression under hydrogen peroxide, drought, and high salt treatments. Moreover, OsMSL16/27/33 displayed significant expression under ABA and drought treatments. Nevertheless, their responses were regulated by light. The expression levels of the 12 chosen OsMSLs differed between light and dark conditions. In conclusion, our results helped elucidate the biological functions of rice trihelix genes and provided a theoretical basis for further characterizing their biological roles in responding to abiotic stresses.

Nitrogen (N) is the mineral element with the largest demand for crops, contributing up to 40% to 50% to the final yield. There is an increasing demand for breeding high N use efficiency (NUE) cultivars using high NUE genes due to the urgent requirements for sustainable agricultural development. Here, GWAS was conducted and OsNLP6 was identified, which encodes a NIN‐like protein (NLP) transcription factor, as a key regulatory gene which was highly expressed in roots for rice NUE and yield formation. OsNLP6 positively regulates N uptake by activating the transcription of OsPTR2.4 , which affects the nitrate transporter. The expression of this molecular module was significantly induced by low N stress. OsNLP6 ^HapII was identified as an elite haplotype with high‐NUE, which was mainly found in the Xian rice subgroup derived from Southeast Asia, and could enhance N uptake and effective panicle number per plant, thereby boosting rice yield and NUE. Introducing OsNLP6 ^HapII into the Geng rice subgroup can be a potential strategy for marker‐assisted selection breeding in rice. Overall, the present study highlights that the OsNLP6 ‐ OsPTR2.4 molecular module can be used to breed rice cultivars with higher NUE in order to achieve agricultural sustainability.

Nitrogen is not only a macronutrient essential for crop growth and development, but also one of the most critical nutrients in farmland ecosystem. Insufficient nitrogen supply will lead to crop yield reduction, while excessive application of nitrogen fertilizer will cause agricultural and eco-environment damage. Therefore, mining low-nitrogen tolerant rice genes and improving nitrogen use efficiency are of great significance to the sustainable development of agriculture. This study was conducted by Genome-wide association study on a basis of two root morphological traits (root length and root diameter) and 788,396 SNPs of a natural population of 295 rice varieties. The transcriptome of low-nitrogen tolerant variety (Longjing 31) and low-nitrogen sensitive variety (Songjing 10) were sequenced between low and high nitrogen treatments. A total of 35 QTLs containing 493 genes were mapped. 3085 differential expressed genes were identified. Among these 493 genes, 174 genes showed different haplotype patterns. There were significant phenotype differences among different haplotypes of 58 genes with haplotype differences. These 58 genes were hypothesized as candidate genes for low nitrogen tolerance related to root morphology. Finally, six genes (Os07g0471300, Os11g0230400, Os11g0229300, Os11g0229400, Os11g0618300 and Os11g0229333) which expressed differentially in Longjing 31 were defined as more valuable candidate genes for low-nitrogen tolerance. The results revealed the response characteristics of rice to low-nitrogen, and provided insights into regulatory mechanisms of rice to nitrogen deficiency.

A bioassay-guided fractionation of the ethyl acetate extract from the twigs of the hybrid poplar ‘Neva’, Populus nigra L. × Populus deltoides Marsh, led to the isolation of three flavonoids, which were identified by means of spectrometric and physicochemical analysis as 5-hydroxy-7-methoxy-flavone (1), 5,7-dihydoxy-flavone (2) and 5,7-dihydroxy-flavonol (3). These compounds were further screened for their antimicrobial activity against plant pathogens, including three bacteria (Pseudomonas lachrymans, Ralstonia solanacearum and Xanthomonas vesicatoria) and one fungus (Magnaporthe oryzae). Compounds 2 and 3 showed significant antibacterial activity, with minimum inhibitory concentrations (MICs) ranging from 15 to 25 µg mL⁻¹, and median inhibitory concentrations (IC₅₀ values) from 4 to 18 µg mL⁻¹. The results obtained provide promising baseline information for the potential use of the extract and flavonoids from this plant as antimicrobial agents to help control plant diseases.

The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.

Nitrogen is an important nutrient for plant growth and essential metabolic processes. Roots integrally obtain nutrients from soil and are closely related to the growth and development of plants. In this study, the morphological analysis of rice root tissues collected at different time points under low-nitrogen and normal nitrogen conditions demonstrated that, compared with normal nitrogen treatment, the root growth and nitrogen use efficiency (NUE) of rice under low-nitrogen treatment were significantly improved. To better understand the molecular mechanisms of the rice root system’s response to low-nitrogen conditions, a comprehensive transcriptome analysis of rice seedling roots under low-nitrogen and control conditions was conducted in this study. As a result, 3171 differentially expressed genes (DEGs) were identified. Rice seedling roots enhance NUE and promote root development by regulating the genes related to nitrogen absorption and utilization, carbon metabolism, root growth and development, and phytohormones, thereby adapting to low-nitrogen conditions. A total of 25,377 genes were divided into 14 modules using weighted gene co-expression network analysis (WGCNA). Two modules were significantly associated with nitrogen absorption and utilization. A total of 8 core genes and 43 co-expression candidates related to nitrogen absorption and utilization were obtained in these two modules. Further studies on these genes will contribute to the understanding of low-nitrogen adaptation and nitrogen utilization mechanisms in rice.