The implantable artificial hair was prepared by grafting collagen on the surface of poly(ethylene terephthalate)(PET)to improve its biocompatibility.Acrylic acid(AAc)was used to modify the surface properties of PET fi...The implantable artificial hair was prepared by grafting collagen on the surface of poly(ethylene terephthalate)(PET)to improve its biocompatibility.Acrylic acid(AAc)was used to modify the surface properties of PET firstly,and then collagen was grafted on the PET-AAc surface.The concentration of collagen solution was discussed to graft more collagen on PET surface.Composites were well characterized by scanning electron microscopy(SEM),Fourier transform infrared spectroscopy(FTIR)and X-ray photoelectron spectroscopy(XPS).SEM indicated that collagen with about 3.07μm thickness was coated on PET surface when the concentration of collagen solution was 1.0 mg/m L.FTIR and XPS showed AAc and collagen were both on the surface of PET monofilaments.The optimized concentration of collagen solution was 1.0 mg/m L,resulting in the most grafting density of 3.46μg/cm^(2).It can be concluded that a large amount of collagen is coated on PET surface.展开更多
A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classif...A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM clas- sifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew’s correlation coefficient (MCC) of 0.84. Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.展开更多
Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspe...Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.展开更多
基金Beijing Municipal Commission of Science and Technology Plan Projects,China(No.KYTG02170206/016)Open Project of Beijing Key Laboratory of Clothing Materials R&D and Assessment,China(No.KYTG02170205)
文摘The implantable artificial hair was prepared by grafting collagen on the surface of poly(ethylene terephthalate)(PET)to improve its biocompatibility.Acrylic acid(AAc)was used to modify the surface properties of PET firstly,and then collagen was grafted on the PET-AAc surface.The concentration of collagen solution was discussed to graft more collagen on PET surface.Composites were well characterized by scanning electron microscopy(SEM),Fourier transform infrared spectroscopy(FTIR)and X-ray photoelectron spectroscopy(XPS).SEM indicated that collagen with about 3.07μm thickness was coated on PET surface when the concentration of collagen solution was 1.0 mg/m L.FTIR and XPS showed AAc and collagen were both on the surface of PET monofilaments.The optimized concentration of collagen solution was 1.0 mg/m L,resulting in the most grafting density of 3.46μg/cm^(2).It can be concluded that a large amount of collagen is coated on PET surface.
基金Project (No. 20542006) supported by the National Natural ScienceFoundation of China
文摘A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM clas- sifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew’s correlation coefficient (MCC) of 0.84. Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.
基金National Natural Science Foundations of China(Nos.61503116,61402007)Foundation for Young Talents in the Colleges of Anhui Province Committee,China(No.2013SQRL097ZD)+1 种基金Natural Science Foundation of Anhui Educational Committee,China(No.KJ2014A198)Natural Science Foundation of Anhui Province,China(No.1408085QF108)
文摘Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.
