Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insigh...Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effective MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models’generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.展开更多
探究了采用BS EN ISO 14362-1:2017标准方法分析MDI型聚氨酯制品中禁用偶氮染料分解产物4,4′-二氨基二苯甲烷(MDA)检出率较高的原因,通过对标准方法前处理步骤的逐步验证,确认标准前处理方法pH=6柠檬酸盐缓冲溶液70℃下回流处理操作步...探究了采用BS EN ISO 14362-1:2017标准方法分析MDI型聚氨酯制品中禁用偶氮染料分解产物4,4′-二氨基二苯甲烷(MDA)检出率较高的原因,通过对标准方法前处理步骤的逐步验证,确认标准前处理方法pH=6柠檬酸盐缓冲溶液70℃下回流处理操作步骤可能会导致MDA检出,最终导致MDI型聚氨酯样品中偶氮染料的假阳性检出或是检出结果偏高。展开更多
以液化M D I和BDO(1,4-丁二醇)为硬段,分别以聚乙二醇(PEG),聚己二酸乙二醇酯(PEAG),聚己二酸丁二醇酯(PBAG),聚己二酸己二醇酯(PHAG),聚己内酯(PCL)为软段合成了聚氨酯形状记忆材料。通过FT-IR,DSC等考察了它们的结构,比较了聚乙二醇...以液化M D I和BDO(1,4-丁二醇)为硬段,分别以聚乙二醇(PEG),聚己二酸乙二醇酯(PEAG),聚己二酸丁二醇酯(PBAG),聚己二酸己二醇酯(PHAG),聚己内酯(PCL)为软段合成了聚氨酯形状记忆材料。通过FT-IR,DSC等考察了它们的结构,比较了聚乙二醇聚氨酯(EGPU),聚己二酸乙二醇酯聚氨酯(EAPU),聚己二酸丁二醇酯聚氨酯(BAPU),聚己二酸己二醇酯聚氨酯(HAPU)及聚己内酯聚氨酯(CLPU)的形状记忆性能和力学性能,研究表明,PHAG是液化M D I基形状记忆聚氨酯软段的最佳原料。展开更多
基金supported by the Natural Science Foundation of Wenzhou University of Technology,China(Grant No.:ky202211).
文摘Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effective MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models’generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.
文摘探究了采用BS EN ISO 14362-1:2017标准方法分析MDI型聚氨酯制品中禁用偶氮染料分解产物4,4′-二氨基二苯甲烷(MDA)检出率较高的原因,通过对标准方法前处理步骤的逐步验证,确认标准前处理方法pH=6柠檬酸盐缓冲溶液70℃下回流处理操作步骤可能会导致MDA检出,最终导致MDI型聚氨酯样品中偶氮染料的假阳性检出或是检出结果偏高。
文摘以液化M D I和BDO(1,4-丁二醇)为硬段,分别以聚乙二醇(PEG),聚己二酸乙二醇酯(PEAG),聚己二酸丁二醇酯(PBAG),聚己二酸己二醇酯(PHAG),聚己内酯(PCL)为软段合成了聚氨酯形状记忆材料。通过FT-IR,DSC等考察了它们的结构,比较了聚乙二醇聚氨酯(EGPU),聚己二酸乙二醇酯聚氨酯(EAPU),聚己二酸丁二醇酯聚氨酯(BAPU),聚己二酸己二醇酯聚氨酯(HAPU)及聚己内酯聚氨酯(CLPU)的形状记忆性能和力学性能,研究表明,PHAG是液化M D I基形状记忆聚氨酯软段的最佳原料。
