Liver injury is a major health issue with significant implications for liver function and overall well-being,but precise mechanisms of the N^(6-)methyladenine(m^(6)A)reader YTHDF3 in liver injury remain severely under...Liver injury is a major health issue with significant implications for liver function and overall well-being,but precise mechanisms of the N^(6-)methyladenine(m^(6)A)reader YTHDF3 in liver injury remain severely understudied.Here,we discovered that Ythdf3 knockout exacerbated CCL4-induced liver injury with a reduction in functional hepatocytes and liver stem cells using single cell RNA-sequencing and organoid culture.Furthermore,Mettl14 and YTHDF3-dependent RNA m^(6)A dysregulation induced DNA damage.Moreover,we found YTHDF3 could bind and modulate CCAAT/enhancer-binding protein-alpha(CEBPA)translation in an m^(6)A-dependent manner.Mechanistically,knockout of Ythdf3 impeded the translation of CEBPA,subsequently inhibiting the expression of poly(ADP-ribose)(PAR)polymerase-1(PARP1)and Peroxiredoxin 2(PRDX2).This inhibition promoted DNA damage and genomic instability,ultimately exacerbating liver damage.This work uncovers an essential role of m^(6)A/YTHDF3/CEBPA regulatory axes in governing cell fates and genomic stability,thereby preventing liver injury.Importantly,these findings offer potential therapeutic avenues for targeting YTHDF3 and CEBPA in the treatment of liver injuryrelated diseases.展开更多
Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying N...Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying National Station and the Guyuan Greenhouse Station between April 2024 and April 2025,this study employed machine learning methods to develop wind speed prediction models based on BP neural network,support vector machine,and random forest(referred to as BP,SVM,and RF models),aiming to provide references for local disaster prevention and mitigation.The results indicate that:1)Wind speed at the Guyuan Greenhouse Station exhibits the strongest correlation with that at the National Station(0.489-0.595),followed by temperature and 24-hour precipitation(0.116-0.336).2)The mean absolute error(MAE)of the BP,RF,and SVM models at all heights is below 1.5 m/s,the root mean square error(RMSE)is under 2.1 m/s,and the forecast accuracy(FA)exceeds 75%,indicating satisfactory model performance.Compared to 3 m,the MAE and RMSE of 0.5 m are larger,while the FA is smaller.This indicates that the wind speed of 0.5 m is close to the ground,and is more affected by surface roughness and turbulence effects,resulting in greater randomness and making the model more difficult.3)Based on case analyses of May 10 and May 1,2024,the overall simulation performance ranks as“RF model>SVM model>BP model”;however,the SVM model demonstrates higher accuracy in simulating strong wind events.展开更多
基金supported by the National Key Research and Development Program of China(2022YFD130040307)the Fundamental Research Funds for the Central Universities(KYCXJC2024001)the National Institute of Mental Health(R01 MH122142,B.C.D.)of the National Institutes of Health(NIH)。
文摘Liver injury is a major health issue with significant implications for liver function and overall well-being,but precise mechanisms of the N^(6-)methyladenine(m^(6)A)reader YTHDF3 in liver injury remain severely understudied.Here,we discovered that Ythdf3 knockout exacerbated CCL4-induced liver injury with a reduction in functional hepatocytes and liver stem cells using single cell RNA-sequencing and organoid culture.Furthermore,Mettl14 and YTHDF3-dependent RNA m^(6)A dysregulation induced DNA damage.Moreover,we found YTHDF3 could bind and modulate CCAAT/enhancer-binding protein-alpha(CEBPA)translation in an m^(6)A-dependent manner.Mechanistically,knockout of Ythdf3 impeded the translation of CEBPA,subsequently inhibiting the expression of poly(ADP-ribose)(PAR)polymerase-1(PARP1)and Peroxiredoxin 2(PRDX2).This inhibition promoted DNA damage and genomic instability,ultimately exacerbating liver damage.This work uncovers an essential role of m^(6)A/YTHDF3/CEBPA regulatory axes in governing cell fates and genomic stability,thereby preventing liver injury.Importantly,these findings offer potential therapeutic avenues for targeting YTHDF3 and CEBPA in the treatment of liver injuryrelated diseases.
基金supported by Ningxia Natural Science Foundation Project(2023AAC02088)Liangshan Prefecture 2023 Annual Science and Technology Planning Project(23ZDYF0182).
文摘Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying National Station and the Guyuan Greenhouse Station between April 2024 and April 2025,this study employed machine learning methods to develop wind speed prediction models based on BP neural network,support vector machine,and random forest(referred to as BP,SVM,and RF models),aiming to provide references for local disaster prevention and mitigation.The results indicate that:1)Wind speed at the Guyuan Greenhouse Station exhibits the strongest correlation with that at the National Station(0.489-0.595),followed by temperature and 24-hour precipitation(0.116-0.336).2)The mean absolute error(MAE)of the BP,RF,and SVM models at all heights is below 1.5 m/s,the root mean square error(RMSE)is under 2.1 m/s,and the forecast accuracy(FA)exceeds 75%,indicating satisfactory model performance.Compared to 3 m,the MAE and RMSE of 0.5 m are larger,while the FA is smaller.This indicates that the wind speed of 0.5 m is close to the ground,and is more affected by surface roughness and turbulence effects,resulting in greater randomness and making the model more difficult.3)Based on case analyses of May 10 and May 1,2024,the overall simulation performance ranks as“RF model>SVM model>BP model”;however,the SVM model demonstrates higher accuracy in simulating strong wind events.
