A disintegrin and metalloprotease 17(ADAM17)is a membrane-bound enzyme that cleaves cell-surface proteins.Here,we discovered that neuronal ADAM17-mediated signaling supports the reduction of inhibitory presynaptic inp...A disintegrin and metalloprotease 17(ADAM17)is a membrane-bound enzyme that cleaves cell-surface proteins.Here,we discovered that neuronal ADAM17-mediated signaling supports the reduction of inhibitory presynaptic inputs to the pre-sympathetic glutamatergic neural hub,located in the paraventricular nucleus of the hypothalamus(PVN),upon stimulation by angiotensin II(Ang-II).For Ang-II-induced disinhibition,targeting microglial migration had an effect similar to ADAM17 knockout in glutamatergic neurons.Ang-II promoted neuron-mediated chemotaxis of microglia via neuronal CX3CL1 and ADAM17.Inhibiting microglial chemotaxis by targeting CX3CR1 abolished the Ang-II-induced microglial displacement of GABAergic presynaptic terminals and significantly blunted Ang-II’s pressor response.Using conditional and targeted knockout models of ADAM17,an increase in the contact between pre-sympathetic neurons and reactive microglia in the PVN was demonstrated to be neuronal ADAM17-dependent during the developmental stage of salt-sensitive hypertension.Collectively,this study provides evidence that neuronal ADAM17-mediated microglial chemotaxis facilitates the disinhibition of pre-sympathetic glutamatergic tone upon hormonal stimulation.展开更多
In this paper,we propose a learning algorithm termed linear multistep adaptive moment(LMAdam) to enhance the adaptive moment(Adam) algorithm for machine learning.Considering Adam as a single-step discretization of its...In this paper,we propose a learning algorithm termed linear multistep adaptive moment(LMAdam) to enhance the adaptive moment(Adam) algorithm for machine learning.Considering Adam as a single-step discretization of its continuous counterpart,we develop the LMAdam algorithm based on a linear multistep discretization scheme.We design a feedforward neural network for learning the coefficients of the multistep terms with ensured consistency and select the coefficients to ensure zero stability of the multistep terms.We experimentally demonstrate the superiority of the LMAdam via extensive experimentation on benchmark datasets for training various deep neural networks in three applications.展开更多
基金supported by the National Natural Science Foundation of China(82100454,32271016,82101586,and 81872563)the National Heart,Lung,Blood,and Sleep Institute(HL163588).
文摘A disintegrin and metalloprotease 17(ADAM17)is a membrane-bound enzyme that cleaves cell-surface proteins.Here,we discovered that neuronal ADAM17-mediated signaling supports the reduction of inhibitory presynaptic inputs to the pre-sympathetic glutamatergic neural hub,located in the paraventricular nucleus of the hypothalamus(PVN),upon stimulation by angiotensin II(Ang-II).For Ang-II-induced disinhibition,targeting microglial migration had an effect similar to ADAM17 knockout in glutamatergic neurons.Ang-II promoted neuron-mediated chemotaxis of microglia via neuronal CX3CL1 and ADAM17.Inhibiting microglial chemotaxis by targeting CX3CR1 abolished the Ang-II-induced microglial displacement of GABAergic presynaptic terminals and significantly blunted Ang-II’s pressor response.Using conditional and targeted knockout models of ADAM17,an increase in the contact between pre-sympathetic neurons and reactive microglia in the PVN was demonstrated to be neuronal ADAM17-dependent during the developmental stage of salt-sensitive hypertension.Collectively,this study provides evidence that neuronal ADAM17-mediated microglial chemotaxis facilitates the disinhibition of pre-sympathetic glutamatergic tone upon hormonal stimulation.
基金supported in part by the National Natural Science Foundation of China(62506148 and 62476115)the Fundamental Research Funds for the Central Universities(lzujbky-2025-pd05 and lzujbky-2025-ytB01)+2 种基金the Research Grants Council of the Hong Kong Special Administrative Region of China(AoE/E-407/24-N and C1013-24G)the Postdoctoral Fellowship Program(Grade C) of China Postdoctoral Science Foundation(GZC20251039)the Supercomputing Center of Lanzhou University。
文摘In this paper,we propose a learning algorithm termed linear multistep adaptive moment(LMAdam) to enhance the adaptive moment(Adam) algorithm for machine learning.Considering Adam as a single-step discretization of its continuous counterpart,we develop the LMAdam algorithm based on a linear multistep discretization scheme.We design a feedforward neural network for learning the coefficients of the multistep terms with ensured consistency and select the coefficients to ensure zero stability of the multistep terms.We experimentally demonstrate the superiority of the LMAdam via extensive experimentation on benchmark datasets for training various deep neural networks in three applications.
