Liver receptor homolog-1(LRH-1)is an orphan nuclear receptor that is critical for the growth and proliferation of cancer cells and other biological processes,including lipid transportation and metabolism,sexual determ...Liver receptor homolog-1(LRH-1)is an orphan nuclear receptor that is critical for the growth and proliferation of cancer cells and other biological processes,including lipid transportation and metabolism,sexual determination and steroidogenesis.However,because homozygous lrh-1mice die in utero,the regulatory mechanisms involved in embryonic development mediated by this receptor are poorly understood.In the present study,we performed transcription activator-like effector nuclease(TALEN)-mediated loss-of-function assays,taking advantage of zebrafish external fertilization,to investigate the function of lrh-1.The digestive organs were affected by lrh-1 depletion as a result of cellcycle arrest(at the checkpoint of G1 to S phase),but not cell apoptosis.Biochemical analysis revealed that LRH-1 augments the transcriptional activity of b-catenin 1 and 2 via physical interactions.Screening the specific ligand(s)sensed by LRH-1 during organogenesis revealed that phosphatidylcholine(PC),a potential ligand,is the upstream target of LRH-1 during endoderm development.These data provide evidence for the crosstalk between the PC/LRH-1 and Wnt/β-catenin signaling pathways during the expansion growth of endoderm organs.展开更多
In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical simulations.Specifically,optimization-based smoothing is used for high-qual...In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical simulations.Specifically,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational overhead.Pioneer works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes.However,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence problem.Additionally,the required labeled high-quality meshes further limit the applicability of the proposed method.In this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh smoothing.GMSNet adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node position.During smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements.With a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during training.We compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle meshes.Experimental results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.展开更多
Combustion of agricultural organic solid waste(AOSW)was an ideal solution for their resource utilization in view of their massive annual production and great potential for reduction of fossil fuel utilization.However,...Combustion of agricultural organic solid waste(AOSW)was an ideal solution for their resource utilization in view of their massive annual production and great potential for reduction of fossil fuel utilization.However,high alkali and alkaline earth metals(AAEMs)content in the feedstock can arose severe fouling and slagging issues and thus prohibiting its vast utilization.In this study,a semi-continuous water washing method was proposed to preliminarily remove AAEMs from agricultural organic solid waste and its effects on the combustion behaviors of washed solid product were investigated.Results showed that the combustion index S were improved to 2.63×10-6,over 68%of the total ashes were removed from the cotton stalk,and 96.3%,89.0%and 74.7%of K,Na and Mg were effectively removed,respectively.Moreover,the softening temperature of low temperature ash from the washed sample was as high as 1450◦C,538◦C higher than the low temperature ash from the original sample;the base acid ratio and fouling index were improved from high slagging and fouling risk(1.7 and 90.8)of the original organic solid waste to low and medium risk(0.4 and 3.5),respectively.All these results signified the contributing effect of proposed semi-continuous water washing method on the combustion of agricultural organic solid waste.In a word,this study provided a promising method for fouling and slagging inhibition during the agricultural organic solid waste combustion.展开更多
基金financially supported by the grants from the National Natural Science Foundation of China (No.31530077 to Z.Y. and No.31501857 to G.Z.)the National Basic Research Program of China (973 Program) (No.2014CB138602 to Z.Y.)
文摘Liver receptor homolog-1(LRH-1)is an orphan nuclear receptor that is critical for the growth and proliferation of cancer cells and other biological processes,including lipid transportation and metabolism,sexual determination and steroidogenesis.However,because homozygous lrh-1mice die in utero,the regulatory mechanisms involved in embryonic development mediated by this receptor are poorly understood.In the present study,we performed transcription activator-like effector nuclease(TALEN)-mediated loss-of-function assays,taking advantage of zebrafish external fertilization,to investigate the function of lrh-1.The digestive organs were affected by lrh-1 depletion as a result of cellcycle arrest(at the checkpoint of G1 to S phase),but not cell apoptosis.Biochemical analysis revealed that LRH-1 augments the transcriptional activity of b-catenin 1 and 2 via physical interactions.Screening the specific ligand(s)sensed by LRH-1 during organogenesis revealed that phosphatidylcholine(PC),a potential ligand,is the upstream target of LRH-1 during endoderm development.These data provide evidence for the crosstalk between the PC/LRH-1 and Wnt/β-catenin signaling pathways during the expansion growth of endoderm organs.
基金supported by the National Key Research and Development Program of China(No.2021YFB0300101)the Youth Foundation of National University of Defense Technology,China(No.ZK2023-11)the National Natural Science Foundation of China(No.12102467)。
文摘In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical simulations.Specifically,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational overhead.Pioneer works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes.However,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence problem.Additionally,the required labeled high-quality meshes further limit the applicability of the proposed method.In this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh smoothing.GMSNet adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node position.During smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements.With a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during training.We compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle meshes.Experimental results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.
基金the financial supports provided by the National Key Research and Development Program of China(2019YFC190252).
文摘Combustion of agricultural organic solid waste(AOSW)was an ideal solution for their resource utilization in view of their massive annual production and great potential for reduction of fossil fuel utilization.However,high alkali and alkaline earth metals(AAEMs)content in the feedstock can arose severe fouling and slagging issues and thus prohibiting its vast utilization.In this study,a semi-continuous water washing method was proposed to preliminarily remove AAEMs from agricultural organic solid waste and its effects on the combustion behaviors of washed solid product were investigated.Results showed that the combustion index S were improved to 2.63×10-6,over 68%of the total ashes were removed from the cotton stalk,and 96.3%,89.0%and 74.7%of K,Na and Mg were effectively removed,respectively.Moreover,the softening temperature of low temperature ash from the washed sample was as high as 1450◦C,538◦C higher than the low temperature ash from the original sample;the base acid ratio and fouling index were improved from high slagging and fouling risk(1.7 and 90.8)of the original organic solid waste to low and medium risk(0.4 and 3.5),respectively.All these results signified the contributing effect of proposed semi-continuous water washing method on the combustion of agricultural organic solid waste.In a word,this study provided a promising method for fouling and slagging inhibition during the agricultural organic solid waste combustion.
