The MADS-box gene family is one of the largest families in plants and plays an important roles in floral development. The MADS-box family includes the SRF-like domain and K-box domain. It is considered that the MADS-box gene family encodes a DNA-binding domain that is generally related to transcription factors, and plays important roles in regulating floral development. Our study identified 211 MADS-box protein sequences in the Zea mays proteome and renamed all the genes based on the gene annotations. All the 211 MADS-box protein sequences were coded by 98 expressed genes. Phylogenetic analysis of the MADS-box genes showed that all the family members were categorized into five subfamilies: MIKC-type, Mα, Mβ, Mγ, and Mδ. Gene duplications are regarded as products of several types of errors during the period of DNA replication and reconstruction; in our study all the 98 MADS-box genes contained 22 pairs of segmentally duplicated events which were distributed on 10 chromosomes. We compared expression data in different tissues from the female spikelet, silk, pericarp aleurone, ear primordium, leaf zone, vegetative meristem, internode, endosperm crown, mature pollen, embryo, root cortex, secondary root, germination kernels, primary root, root elongation zone, and root meristem. According to analysis of gene ontology pathways, we found a total of 41 pathways in which MADS-box genes in maize are involved. All the studies we conducted provided an overview of MADS-box gene family members in maize and showed multiple functions as transcription factors. The related research of MADS-box domains has provided the theoretical basis of MADS-box domains for agricultural applications.

Cancers of the digestive system are malignant diseases. Our study focused on colon cancer, esophageal cancer (ESCC), rectal cancer, gastric cancer (GC), and rectosigmoid junction cancer to identify possible biomarkers for these diseases. The transcriptome data were downloaded from the TCGA database (The Cancer Genome Atlas Program), and a network was constructed using the WGCNA algorithm. Two significant modules were found, and coexpression networks were constructed. CytoHubba was used to identify hub genes of the two networks. GO analysis suggested that the network genes were involved in metabolic processes, biological regulation, and membrane and protein binding. KEGG analysis indicated that the significant pathways were the calcium signaling pathway, fatty acid biosynthesis, and pathways in cancer and insulin resistance. Some of the most significant hub genes werehsa-let-7b-3p,hsa-miR-378a-5p,hsa-miR-26a-5p,hsa-miR-382-5p, andhsa-miR-29b-2-5pandSECISBP2 L,NCOA1,HERC1,HIPK3, andMBNL1, respectively. These genes were predicted to be associated with the tumor prognostic reference for this patient population.

Rice blast caused by Magnaporthe oryzae is one of the most serious rice diseases worldwide. The early indica rice thermosensitive genic male sterile (TGMS) line HD9802S has the characteristics of stable fertility, reproducibility, a high outcrossing rate, excellent rice quality, and strong combining ability. However, this line exhibits poor blast resistance and is highly susceptible to leaf and neck blasts. In this study, backcross introduction, molecular marker-assisted selection, gene chipping, anther culture, and resistance identification in the field were used to introduce the broad-spectrum blast-resistance gene R6 into HD9802S to improve its rice blast resistance. Six induction media were prepared by varying the content of each component in the culture medium. Murashige and Skoog’s medium with 3 mg/L 2,4-dichlorophenoxyacetic acid, 2 mg/L 1-naphthaleneacetic acid, and 1 mg/L kinetin and N6 medium with 800 mg/L casein hydrolysate, 600 mg/L proline, and 500 mg/L glutamine could improve the callus induction rate and have a higher green seedling rate and a lower white seedling rate. Compared to HD9802S, two doubled haploid lines containing R6 with stable fertility showed significantly enhanced resistance to rice blast and no significant difference in spikelet number per panicle, 1000-grain weight, or grain shape. Our findings highlight a rapid and effective method for improving rice blast resistance in TGMS lines.

Depression is increasingly recognized as a disorder not only of the brain but also of systemic metabolic dysfunction, particularly involving the gut microb

Cold-start compound-protein interaction (CPI) prediction remains a major challenge in drug discovery, as models must generalize to unseen compounds, targets, or their combinations beyond the training distribution. Most existing methods rely on shallow feature fusion and primarily fit observed interaction patterns, failing to capture semantic correspondence between heterogeneous molecular entities and thus generalize poorly under cold-start conditions. We propose SACS-CPI, a multimodal framework for cold-start CPI prediction based on large language model (LLM)-driven semantic alignment. By mapping compounds and proteins into a shared semantic space, SACS-CPI strengthens semantic consistency between interacting entities, reduces dependence on observed interaction pairs, and improves prediction for unseen compound-protein combinations. The framework further integrates general semantic knowledge learned by large language models with task-specific features through dynamic gating, while bidirectional cross-attention and bilinear pooling are used to capture high-order interaction patterns. Extensive evaluations across multiple benchmark datasets demonstrate that SACS-CPI consistently outperforms state-of-the-art methods, with robust gains under compound cold-start, protein cold-start, and blind-start settings. These results indicate that semantic alignment provides an effective inductive bias for cold-start CPI prediction. The code and data are available at: https://github.com/yangkun021224/SACS-CPI/.