The EPIDERMAL PATTERNING FACTOR (EPF) plays a crucial role in plant response to abiotic stress. While the EPF has been extensively studied in model plants such as Arabidopsis thaliana, there is a lack of research on identifying EPF genes in the whole sorghum genome and its response to drought stress. In this study, we employed bioinformatics tools to identify 12 EPF members in sorghum. Phylogenetic tree analysis revealed that SbEPFs can be categorized into four branches. Further examination of the gene structure and protein conservation motifs of EPF family members demonstrated the high conservation of the SbEPF sequence. The promoter region of SbEPFs was found to encompass cis-elements responsive to stress and plant hormones. Moreover, real-time fluorescence quantitative results indicated that the SbEPFs have a tissue-specific expression. Under drought stress treatment, most SbEPF members were significantly up-regulated, indicating their potential role in drought response. Our research findings establish a foundation for investigating the function of SbEPFs and offer candidate genes for stress-resistant breeding and enhanced production in sorghum.

Sorghum (Sorghum bicolor), the fifth most important cereal crop globally, serves as a staple food, animal feed, and a bioenergy source. Paclobutrazol-Resistance (PRE) genes play a pivotal role in the response to environmental stress, yet the understanding of their involvement in pest resistance remains limited. In the present study, a total of seven SbPRE genes were found within the sorghum BTx623 genome. Subsequently, their genomic location was studied, and they were distributed on four chromosomes. An analysis of cis-acting elements in SbPRE promoters revealed that various elements were associated with hormones and stress responses. Expression pattern analysis showed differentially tissue-specific expression profiles among SbPRE genes. The expression of some SbPRE genes can be induced by abiotic stress and aphid treatments. Furthermore, through phytohormones and transgenic analyses, we demonstrated that SbPRE4 improves sorghum resistance to aphids by accumulating jasmonic acids (JAs) in transgenic Arabidopsis, giving insights into the molecular and biological function of atypical basic helix-loop-helix (bHLH) transcription factors in sorghum pest resistance.

Background: Accurate sorghum spike detection is critical for monitoring growth conditions, accurately predicting yield, and ensuring food security. Deep learning models have improved the accuracy of spike detection thanks to advances in artificial intelligence. However, the dense distribution of sorghum spikes, variable sizes and complex background information in UAV images make detection and counting difficult.Methods: We propose a multiscale and oriented sorghum spike detection and counting model in UAV images (MOSSNet). The model creates a Deformable Convolution Spatial Attention (DCSA) module to improve the network's ability to capture small sorghum spike features. It also integrated Circular Smooth Labels (CSL) to effectively represent morphological features. The model also employs a Wise IoU-based localization loss function to improve network loss. Results: Results show that MOSSNet accurately counts sorghum spike under field conditions, achieving mAP of 90.3%. MOSSNet shows excellent performance in predicting spike orientation, with RMSEa and MAEa of 14.6 and 12.5 respectively, outperforming other directional detection algorithms. Compared to general object detection algorithms which output horizonal detection boxes, MOSSNet also demonstrates high efficiency in counting sorghum spikes, with RMSE and MAE values of 9.3 and 8.1, respectively.Discussion: Sorghum spikes have a slender morphology and their orientation angles tend to be highly variable in natural environments. MOSSNet 's ability has been proved to handle complex scenes with dense distribution, strong occlusion, and complicated background information. This highlights its robustness and generalizability, making it an effective tool for sorghum spike detection and counting. In the future, we plan to further explore the detection capabilities of MOSSNet at different stages of sorghum growth. This will involve implementing object model improvements tailored to each stage and developing a real-time workflow for accurate sorghum spike detection and counting.