This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices...This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree.展开更多
Synapse organizers are essential for the development,transmission,and plasticity of synapses.Acting as rare synapse suppressors,the MAM domain containing glycosylphosphatidylinositol anchor(MDGA)proteins contributes t...Synapse organizers are essential for the development,transmission,and plasticity of synapses.Acting as rare synapse suppressors,the MAM domain containing glycosylphosphatidylinositol anchor(MDGA)proteins contributes to synapse organization by inhibiting the formation of the synaptogenic neuroligin-neurexin complex.A previous analysis of MDGA2 mice lacking a single copy of Mdga2 revealed upregulated glutamatergic synapses and behaviors consistent with autism.However,MDGA2 is expressed in diverse cell types and is localized to both excitatory and inhibitory synapses.Differentiating the network versus cell-specific effects of MDGA2 loss-of-function requires a cell-type and brain region-selective strategy.To address this,we generated mice harboring a conditional knockout of Mdga2 restricted to CA1 pyramidal neurons.Here we report that MDGA2 suppresses the density and function of excitatory synapses selectively on pyramidal neurons in the mature hippocampus.Conditional deletion of Mdga2 in CA1 pyramidal neurons of adult mice upregulated miniature and spontaneous excitatory postsynaptic potentials,vesicular glutamate transporter 1 intensity,and neuronal excitability.These effects were limited to glutamatergic synapses as no changes were detected in miniature and spontaneous inhibitory postsynaptic potential properties or vesicular GABA transporter intensity.Functionally,evoked basal synaptic transmission and AMPAR receptor currents were enhanced at glutamatergic inputs.At a behavioral level,memory appeared to be compromised in Mdga2 cKO mice as both novel object recognition and contextual fear conditioning performance were impaired,consistent with deficits in long-term potentiation in the CA3-CA1 pathway.Social affiliation,a behavioral analog of social deficits in autism,was similarly compromised.These results demonstrate that MDGA2 confines the properties of excitatory synapses to CA1 neurons in mature hippocampal circuits,thereby optimizing this network for plasticity,cognition,and social behaviors.展开更多
Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvo...Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.展开更多
基金The National Natural Science Foundation of China(No.10801031)
文摘This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree.
基金supported by the National Natural Science Foundation of China(82001203,82173819,81871012,and 81571263)the Scientific Research Fund of Zhejiang Provincial Education Department(Y201839276)+3 种基金the Scientific Research Foundation of Zhejiang University City College(X-202103)the R&D Project of Zhejiang(2022C03034)the Natural Science Foundation of Zhejiang Province(LQ23C090001)a Canada Research Chair Award(P2018-0246).
文摘Synapse organizers are essential for the development,transmission,and plasticity of synapses.Acting as rare synapse suppressors,the MAM domain containing glycosylphosphatidylinositol anchor(MDGA)proteins contributes to synapse organization by inhibiting the formation of the synaptogenic neuroligin-neurexin complex.A previous analysis of MDGA2 mice lacking a single copy of Mdga2 revealed upregulated glutamatergic synapses and behaviors consistent with autism.However,MDGA2 is expressed in diverse cell types and is localized to both excitatory and inhibitory synapses.Differentiating the network versus cell-specific effects of MDGA2 loss-of-function requires a cell-type and brain region-selective strategy.To address this,we generated mice harboring a conditional knockout of Mdga2 restricted to CA1 pyramidal neurons.Here we report that MDGA2 suppresses the density and function of excitatory synapses selectively on pyramidal neurons in the mature hippocampus.Conditional deletion of Mdga2 in CA1 pyramidal neurons of adult mice upregulated miniature and spontaneous excitatory postsynaptic potentials,vesicular glutamate transporter 1 intensity,and neuronal excitability.These effects were limited to glutamatergic synapses as no changes were detected in miniature and spontaneous inhibitory postsynaptic potential properties or vesicular GABA transporter intensity.Functionally,evoked basal synaptic transmission and AMPAR receptor currents were enhanced at glutamatergic inputs.At a behavioral level,memory appeared to be compromised in Mdga2 cKO mice as both novel object recognition and contextual fear conditioning performance were impaired,consistent with deficits in long-term potentiation in the CA3-CA1 pathway.Social affiliation,a behavioral analog of social deficits in autism,was similarly compromised.These results demonstrate that MDGA2 confines the properties of excitatory synapses to CA1 neurons in mature hippocampal circuits,thereby optimizing this network for plasticity,cognition,and social behaviors.
基金Partially Supported by National Natural Science Foundation of China(No.61173102)
文摘Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.
