Although VEGF-B was discovered as a VEGF-A homolog a long time ago,the angiogenic effect of VEGF-B remains poorly understood with limited and diverse findings from different groups.Notwithstanding,drugs that inhibit V...Although VEGF-B was discovered as a VEGF-A homolog a long time ago,the angiogenic effect of VEGF-B remains poorly understood with limited and diverse findings from different groups.Notwithstanding,drugs that inhibit VEGF-B together with other VEGF family members are being used to treat patients with various neovascular diseases.It is therefore critical to have a better understanding of the angiogenic effect of VEGF-B and the underlying mechanisms.Using comprehensive in vitro and in vivo methods and models,we reveal here for the first time an unexpected and surprising function of VEGF-B as an endogenous inhibitor of angiogenesis by inhibiting the FGF2/FGFR1 pathway when the latter is abundantly expressed.Mechanistically,we unveil that VEGF-B binds to FGFR1,induces FGFR1/VEGFR1 complex formation,and suppresses FGF2-induced Erk activation,and inhibits FGF2-driven angiogenesis and tumor growth.Our work uncovers a previously unrecognized novel function of VEGF-B in tethering the FGF2/FGFR1 pathway.Given the anti-angiogenic nature of VEGF-B under conditions of high FGF2/FGFR1 levels,caution is warranted when modulating VEGF-B activity to treat neovascular diseases.展开更多
Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can a...Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.展开更多
基金This study is supported by the State Key Laboratory of Ophthalmology,Zhongshan Ophthalmic Center,Sun Yat-sen University,and Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science,Guangzhou 510060,P.R.Chinathe National Natural Science Foundation of China(82150710555 and 82220108016 to X.Li,81970823 to Jin Yao and 81830013 to J.O.)+4 种基金a Key Research and Development Plan of Shandong Province(2016GSF201100)the Fundamental Research Funds for the Central Universities(19ykpy151)the long-term structural Methusalem funding by the Flemish Government,Belgiumthe Deutsche Forschungsge-meinschaft(Project No.:394046768-SFB1366)the DZHK partner site Mannheim/Heidelberg to H.F.L.,an ERA PerMed 2020 JTC grant“PROGRESS”.
文摘Although VEGF-B was discovered as a VEGF-A homolog a long time ago,the angiogenic effect of VEGF-B remains poorly understood with limited and diverse findings from different groups.Notwithstanding,drugs that inhibit VEGF-B together with other VEGF family members are being used to treat patients with various neovascular diseases.It is therefore critical to have a better understanding of the angiogenic effect of VEGF-B and the underlying mechanisms.Using comprehensive in vitro and in vivo methods and models,we reveal here for the first time an unexpected and surprising function of VEGF-B as an endogenous inhibitor of angiogenesis by inhibiting the FGF2/FGFR1 pathway when the latter is abundantly expressed.Mechanistically,we unveil that VEGF-B binds to FGFR1,induces FGFR1/VEGFR1 complex formation,and suppresses FGF2-induced Erk activation,and inhibits FGF2-driven angiogenesis and tumor growth.Our work uncovers a previously unrecognized novel function of VEGF-B in tethering the FGF2/FGFR1 pathway.Given the anti-angiogenic nature of VEGF-B under conditions of high FGF2/FGFR1 levels,caution is warranted when modulating VEGF-B activity to treat neovascular diseases.
基金financial supports from National Natural Science Foundation of China(No.62205172)Huaneng Group Science and Technology Research Project(No.HNKJ22-H105)Tsinghua University Initiative Scientific Research Program and the International Joint Mission on Climate Change and Carbon Neutrality。
文摘Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.
基金supported by the National Natural Science Foundation of China(11902243 and 51903124)the Young Elite Scientist Sponsorship Program by CAST(2019QNRC001)+2 种基金the“1000-Plan Program”of Shaanxi Provincethe“Young Talent Support Plan”of Xi’an Jiaotong UniversityInitiative Funds of Scientific Research for Metasequoia Talent(163105049)。
