Hyaluronan binding protein 1 (HABP1) is a negatively charged multifunctional mammalian protein with a unique structural fold. Despite the fact that HABP1 possesses mitochondrial localization signal, it has also been l...Hyaluronan binding protein 1 (HABP1) is a negatively charged multifunctional mammalian protein with a unique structural fold. Despite the fact that HABP1 possesses mitochondrial localization signal, it has also been localized to other cellular compartments. Using indirect immunofluorescence, we examined the sub-cellular localization of HABP1 and its dynamics during mitosis. We wanted to determine whether it distributes in any distinctive manner after mitotic nuclear envelope disassembly or is dispersed randomly throughout the cell. Our results reveal the golgi localization of HABP1 and demonstrate its complete dispersion throughout the cell during mitosis. This distinctive distribution pattern of HABP1 during mitosis resembles its ligand hyaluronan, suggesting that in concert with each other the two molecules play critical roles in this dynamic process.展开更多
Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of sing...Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.展开更多
文摘Hyaluronan binding protein 1 (HABP1) is a negatively charged multifunctional mammalian protein with a unique structural fold. Despite the fact that HABP1 possesses mitochondrial localization signal, it has also been localized to other cellular compartments. Using indirect immunofluorescence, we examined the sub-cellular localization of HABP1 and its dynamics during mitosis. We wanted to determine whether it distributes in any distinctive manner after mitotic nuclear envelope disassembly or is dispersed randomly throughout the cell. Our results reveal the golgi localization of HABP1 and demonstrate its complete dispersion throughout the cell during mitosis. This distinctive distribution pattern of HABP1 during mitosis resembles its ligand hyaluronan, suggesting that in concert with each other the two molecules play critical roles in this dynamic process.
基金173 Basic Strengthening ProgramXi'an Science and Technology Plan(21ZCZZHXJS-QCY6-0002)。
文摘Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.
