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Soil salinity is a major factor threatening the production of crops around the world. Smooth bromegrass (Bromus inermis L.) is a high-quality grass in northern and northwestern China. Currently, selecting and utilizing salt-tolerant genotypes is an important way to mitigate the detrimental effects of salinity on crop productivity. In our research, salt-tolerant and salt-sensitive varieties were selected from 57 accessions based on a comprehensive evaluation of 22 relevant indexes, and their salt-tolerance physiological and molecular mechanisms were further analyzed. Results showed significant differences in salt tolerance between 57 genotypes, with Q25 and Q46 considered to be the most salt-tolerant and salt-sensitive accessions, respectively, compared to other varieties. Under saline conditions, the salt-tolerant genotype Q25 not only maintained significantly higher photosynthetic performance, leaf relative water content (RWC), and proline content but also exhibited obviously lower relative conductivity and malondialdehyde (MDA) content than the salt-sensitive Q46 (p < 0.05). The transcriptome sequencing indicated 15,128 differentially expressed genes (DEGs) in Q46, of which 7,885 were upregulated and 7,243 downregulated, and 12,658 DEGs in Q25, of which 6,059 were upregulated and 6,599 downregulated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the salt response differences between Q25 and Q46 were attributed to the variable expression of genes associated with plant hormone signal transduction and MAPK signaling pathways. Furthermore, a large number of candidate genes, related to salt tolerance, were detected, which involved transcription factors (zinc finger proteins) and accumulation of compatible osmolytes (glutathione S-transferases and pyrroline-5-carboxylate reductases), etc. This study offers an important view of the physiological and molecular regulatory mechanisms of salt tolerance in two smooth bromegrass genotypes and lays the foundation for further identification of key genes linked to salt tolerance.

Microstrip filters are widely used in high-frequency circuit design for signal frequency selection. However, designing these filters often requires extensive trial and error to achieve the desired performance metrics, leading to significant time costs. In this work, we propose an automated design flow for hairpin filters, a specific type of microstrip filter. We employ artificial neural network (ANN) modeling techniques to predict the circuit performance of hairpin filters, and leverage the efficiency of low-cost models to deploy reinforcement learning agents. Specifically, we use the proximal policy optimization (PPO) reinforcement learning algorithm to learn abstract design actions for the filters, allowing us to achieve automated optimization design. Through simulation results, we demonstrate the effectiveness of the proposed approach. By optimizing the geometric dimensions, we significantly improve the performance metrics of hairpin filters, and the trained agent successfully meets our specified design goals within 5 to 15 design steps. This work serves as a conceptual validation attempt to apply reinforcement learning techniques and pre-trained ANN models to automate MMIC filter design. It exhibits clear advantages in terms of time-saving and performance efficiency when compared to other optimization algorithms.