Significantly increasing crop yield is a major and worldwide challenge for food supply and security. It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide. Yet, the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery. Here, we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group. We identified the top 24 candi-date high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method, i.e., dynamic cross-tissue (DCT) network analysis. We used one of the candidate genes, OsSPL4, whose function was previously unknown, for gene editing experimental validation of the high yield, and confirmed that OsSPL4 significantly affects panicle branching and increases the rice yield. This study, which included extensive field phenotyping, cross-tissue systems biology analyses, and functional validation, uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice. The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample. DCT can be downloaded from https://github.com/zt-pub/DCT.

Objective:To explore the correlation between the dynamic changes of early immune cells in acute respiratory dis-tress syndrome(ARDS)caused by multiple injuries and lung infection.Methods:Multiple injury patients with ARDS(235 cases)ad-mitted to the First Affiliated Hospital of Hunan University of Chinese Medicine from October 2017 to February 2023 were selected as the research subjects and included them in the training set;according to simple clinical pulmonary infection scores(sCPIS),they were divided into a pulmonary infection group(94 cases,>6 points)and an uninfected group(141 cases,≤6 points).Another multi-ple injury patients with ARDS(78 cases)were selected to be included in the validation set to verify the effectiveness of the prediction model.Dynamic detection of lymphocytes were used flow cytometry.Compared and analyzed the clinical data of two groups of patients.Constructed a joint model and used Cox regression to analyze the relationship.Multi factor Logistic regression analysis of risk factors,construction of a simple risk scoring model and evaluation.Results:As the patient progresses,CD4+and CD8+first decrease and then increased,and the lowest stage was 3～15 d after onset;CD19+was gradually increasing;CD16+gradually decreased and fluctuated within a certain range.The joint model showed that for every 1 piece/μl longitudinal decrease in CD4+,CD8+,CD19+,and CD16+,the risk of pulmonary infection increased by 5.6%,4.1%,3.4% and 1.3%,respectively(P<0.05).Injury severity score(ISS),chest inju-ry as the main factor,emergency surgery,acutephysiology and chronic health evaluation Ⅱ(APACHE Ⅱ),broncho-alveolar lavage fluid(BALF)sTREM-1 level,and CD4+,CD8+,and CD19+level on the 15th day after onset all were independent influencing factors for the occurrence of pulmonary infection(P<0.05).The score of the simple risk scoring model is 0～36.7 points,which could be divid-ed into three risk levels:low(<16 points),medium(16～22 points),and high risk(>22 points);there was no significant difference in the incidence of pulmonary infection between the two episodes of patients(P>0.05).The evaluation results showed that the predic-tive model has good discrimination.Conclusion:The fluctuation of immune cells will increase the risk of pulmonary infection in pa-tients with multiple injuries ARDS;Baseline CD4+,CD8+,and CD19+levels are independent influencing factors for the occurrence of pulmonary infection;controlling high-level immune cells and maintaining stability is crucial for improving prognosis.