This study investigated hypoxia-inducible factor(HIF)-1α-mediated proteomic changes in post-slaughter Tan sheep skeletal muscle and identified energy metabolism biomarkers using the competitive adaptive reweighted sa...This study investigated hypoxia-inducible factor(HIF)-1α-mediated proteomic changes in post-slaughter Tan sheep skeletal muscle and identified energy metabolism biomarkers using the competitive adaptive reweighted sampling(CARS)algorithm.HIF-1αinhibition during early storage attenuated pH decline and significantly increased total colour change(ΔE)(P<0.05)while reducing myofibril fragmentation compared with controls.Proteomic profiling identified 257 differentially expressed proteins enriched in adenosine 5’-monophosphate(AMP)-activated protein kinase(AMPK),glycolysis,and HIF-1 signalling pathways.CARS analysis highlighted lactate dehydrogenase A(LDHA),phosphoglycerate kinase 1(PGK1;glycolytic enzyme),heat shock protein beta-6(HSPB6),and heat shock protein 90 kDa beta 1(HSP90B1)as key energy metabolism biomarkers.The results suggested that HIF-1 stabilised ATP production under hypoxia conditions by suppressing glycogen synthesis,enhancing glycolysis,modulating HSP activity to preserve cellular homeostasis,and influencing cytoskeletal proteins,thereby affecting meat quality.These results provide novel insights into post-mortem muscle energy metabolism regulation and potential targets for meat quality optimisation.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
基金supported by the Innovation Research Group Project of the National Natural Science Foundation of China(No.32260555).
文摘This study investigated hypoxia-inducible factor(HIF)-1α-mediated proteomic changes in post-slaughter Tan sheep skeletal muscle and identified energy metabolism biomarkers using the competitive adaptive reweighted sampling(CARS)algorithm.HIF-1αinhibition during early storage attenuated pH decline and significantly increased total colour change(ΔE)(P<0.05)while reducing myofibril fragmentation compared with controls.Proteomic profiling identified 257 differentially expressed proteins enriched in adenosine 5’-monophosphate(AMP)-activated protein kinase(AMPK),glycolysis,and HIF-1 signalling pathways.CARS analysis highlighted lactate dehydrogenase A(LDHA),phosphoglycerate kinase 1(PGK1;glycolytic enzyme),heat shock protein beta-6(HSPB6),and heat shock protein 90 kDa beta 1(HSP90B1)as key energy metabolism biomarkers.The results suggested that HIF-1 stabilised ATP production under hypoxia conditions by suppressing glycogen synthesis,enhancing glycolysis,modulating HSP activity to preserve cellular homeostasis,and influencing cytoskeletal proteins,thereby affecting meat quality.These results provide novel insights into post-mortem muscle energy metabolism regulation and potential targets for meat quality optimisation.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
