Seed watermelon kernel is a typical complex food with high fat and protein contents.During storage and processing,it is often affected by various factors to undergo interactions between components,which lead to its qu...Seed watermelon kernel is a typical complex food with high fat and protein contents.During storage and processing,it is often affected by various factors to undergo interactions between components,which lead to its quality change.In this experiment,seed watermelon kernels were used as research objects,and the effects of 2-Azobis(2-amidinopropane)dihydrochloride(AAPH)on seed watermelon kernel protein isolates(WMP)were investigated.The structure and digestion characteristics of WMP after oxidation were studied.The results showed that with the increase of AAPH concentration(0.05−5 mol/L),WMP showed obvious aggregation,and its solubility decreased from 6.76 mg/mL to 9.59 mg/mL.The free sulfhydryl content of WMP was 18.24 mmol/g decreased to 11.25 mmol/g,α-helix decreased andβ-sheet decreased in secondary structure,and its disulfide bond increased by 43.06 mmol/g from 39.57 mmol/g,enthalpy(H)and denaturation temperature increased(Td)(P<0.05).By mass spectrometry results of simulated gastric digestion products,it was found that oxidation adversely affected the digestion characteristics of WMP.It can be seen that the lipid oxidation product APPH of seed watermelon kernel can significantly affect the structure and function of the protein extracted from the seed kernel.展开更多
Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the opti...Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.展开更多
In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity mea...In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.展开更多
In this work, we developed a method to efficiently optimize the kernel function for combined data of various different sources with their corresponding kernels being already available. The vectorization of the combine...In this work, we developed a method to efficiently optimize the kernel function for combined data of various different sources with their corresponding kernels being already available. The vectorization of the combined data is achieved by a weighted concatenation of the existing data vectors. This induces a kernel matrix composed of the existing kernels as blocks along the main diagonal, weighted according to the corresponding the subspaces span by the data. The induced block kernel matrix is optimized in the platform of least-squares support vector machines simultaneously as the LS-SVM is being trained, by solving an extended set of linear equations, other than a quadratically constrained quadratic programming as in a previous method. The method is tested on a benchmark dataset, and the performance is significantly improved from the highest ROC score 0.84 using individual data source to ROC score 0.92 with data fusion.展开更多
Hair coloring is widely used by women and men either to change their natural hair color or to delay the onset of gray hair. Oxidative dyes may damage the hair, since chemical and physical procedures are involved to al...Hair coloring is widely used by women and men either to change their natural hair color or to delay the onset of gray hair. Oxidative dyes may damage the hair, since chemical and physical procedures are involved to alter the structure hair and consequently, alterations in its mechanical and of surface properties. One benefit of hair conditioners is to prevent flyaway hair, make the hair “shine”, and protect the hair from further damage. In this research we analyzed the hair protective effect conditioner agents Argania spinosa kernel oil and/or Theobroma grandiflorum seed butter in hair care on Caucasian hair post treatment with hair dye. The hairs were submitted by quantifying protein loss. The samples were classified as: hair untreated (I);hair treated with a commercial oxidative ultra-blond hair dye (II);hair post treatment II and F1: Base hair care formulation (III), hair post treatment II and F2: Base hair care formulation containing 1.0% (w/w) Argania spinosa kernel oil (IV), hair post treatment II and F3: Base hair care formulation containing 1.0% (w/w) Theobroma grandiflorum seed butter (V) and hair post treatment II and F4: Base hair care formulation containing 0.5% (w/w) Argania spinosa kernel oil and 0.5% (w/w) Theobroma grandiflorum seed butter (VI). For the protein loss, the results were: IIA = IIIA > IB = IVB = VB = VIB. Results classified with different letters present statistically significant differents, for α = 5, p ≤ 0.05, n = 6. Based on the results, the incorporation of conditioners agents Argania spinosa kernel oil and/or Theobroma grandiflorum seed butter in base hair care formulation applied in Caucasian hair post treatment with hair dye decreased the damage caused to hair by the coloring process.展开更多
Nitrogen fertilization is one of the greatest challenges associated with the production of biofuel from corn grain. The objective of this research was to determine the effect of N fertilization on the content and yiel...Nitrogen fertilization is one of the greatest challenges associated with the production of biofuel from corn grain. The objective of this research was to determine the effect of N fertilization on the content and yield of oil, protein, and starch in corn grain. The project was done in Southeast Missouri （USA）, from 2007 to 2009 in a silt loam soil. Corn grain contains 3.8-4.2% oil, 6.7%-8.9% protein, 68.0%-70.4% extractable starch, and 76.0%-77.7% total starch. The total starch yield ranged from 2.8 to 7.8 mg.ha1 whereas the extractable starch varied between 2.5 to 7.1 mg-ha1. As the N rate went up, the oil and starch content of the grain decreased, whereas the protein content and the protein, starch, and oil yields increased, reaching their maximum at the N rate corresponding to 179.0 kg N.ha~. The potential ethanol yield varied between 616.2 and 7,035.1 L-ha1 depending on the method of conversion of the starch into ethanol, the year and the N rate （P 〈 0.0001）. The negative correlation between N fertilization rate and starch content suggested that when farmers add too much N to their soil to increase grain yield, they reduce the starch content in those grains, and consequently the conversion into bioethanol. Therefore, for biofuel production to be beneficial for both farmers and the power plant owners, an agreement needs to be made with regard to the use of fertilizers.展开更多
基金This study was supported by National Natural Science Foundation of China(No:31760477)Beijing Advanced Innovation Center for Food Nutrition and Human Health(No:20181007)Youth Science and Technology Innovation,Leader in Corps(No:2016BC001).
文摘Seed watermelon kernel is a typical complex food with high fat and protein contents.During storage and processing,it is often affected by various factors to undergo interactions between components,which lead to its quality change.In this experiment,seed watermelon kernels were used as research objects,and the effects of 2-Azobis(2-amidinopropane)dihydrochloride(AAPH)on seed watermelon kernel protein isolates(WMP)were investigated.The structure and digestion characteristics of WMP after oxidation were studied.The results showed that with the increase of AAPH concentration(0.05−5 mol/L),WMP showed obvious aggregation,and its solubility decreased from 6.76 mg/mL to 9.59 mg/mL.The free sulfhydryl content of WMP was 18.24 mmol/g decreased to 11.25 mmol/g,α-helix decreased andβ-sheet decreased in secondary structure,and its disulfide bond increased by 43.06 mmol/g from 39.57 mmol/g,enthalpy(H)and denaturation temperature increased(Td)(P<0.05).By mass spectrometry results of simulated gastric digestion products,it was found that oxidation adversely affected the digestion characteristics of WMP.It can be seen that the lipid oxidation product APPH of seed watermelon kernel can significantly affect the structure and function of the protein extracted from the seed kernel.
文摘Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.
文摘In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.
文摘In this work, we developed a method to efficiently optimize the kernel function for combined data of various different sources with their corresponding kernels being already available. The vectorization of the combined data is achieved by a weighted concatenation of the existing data vectors. This induces a kernel matrix composed of the existing kernels as blocks along the main diagonal, weighted according to the corresponding the subspaces span by the data. The induced block kernel matrix is optimized in the platform of least-squares support vector machines simultaneously as the LS-SVM is being trained, by solving an extended set of linear equations, other than a quadratically constrained quadratic programming as in a previous method. The method is tested on a benchmark dataset, and the performance is significantly improved from the highest ROC score 0.84 using individual data source to ROC score 0.92 with data fusion.
文摘Hair coloring is widely used by women and men either to change their natural hair color or to delay the onset of gray hair. Oxidative dyes may damage the hair, since chemical and physical procedures are involved to alter the structure hair and consequently, alterations in its mechanical and of surface properties. One benefit of hair conditioners is to prevent flyaway hair, make the hair “shine”, and protect the hair from further damage. In this research we analyzed the hair protective effect conditioner agents Argania spinosa kernel oil and/or Theobroma grandiflorum seed butter in hair care on Caucasian hair post treatment with hair dye. The hairs were submitted by quantifying protein loss. The samples were classified as: hair untreated (I);hair treated with a commercial oxidative ultra-blond hair dye (II);hair post treatment II and F1: Base hair care formulation (III), hair post treatment II and F2: Base hair care formulation containing 1.0% (w/w) Argania spinosa kernel oil (IV), hair post treatment II and F3: Base hair care formulation containing 1.0% (w/w) Theobroma grandiflorum seed butter (V) and hair post treatment II and F4: Base hair care formulation containing 0.5% (w/w) Argania spinosa kernel oil and 0.5% (w/w) Theobroma grandiflorum seed butter (VI). For the protein loss, the results were: IIA = IIIA > IB = IVB = VB = VIB. Results classified with different letters present statistically significant differents, for α = 5, p ≤ 0.05, n = 6. Based on the results, the incorporation of conditioners agents Argania spinosa kernel oil and/or Theobroma grandiflorum seed butter in base hair care formulation applied in Caucasian hair post treatment with hair dye decreased the damage caused to hair by the coloring process.
文摘Nitrogen fertilization is one of the greatest challenges associated with the production of biofuel from corn grain. The objective of this research was to determine the effect of N fertilization on the content and yield of oil, protein, and starch in corn grain. The project was done in Southeast Missouri （USA）, from 2007 to 2009 in a silt loam soil. Corn grain contains 3.8-4.2% oil, 6.7%-8.9% protein, 68.0%-70.4% extractable starch, and 76.0%-77.7% total starch. The total starch yield ranged from 2.8 to 7.8 mg.ha1 whereas the extractable starch varied between 2.5 to 7.1 mg-ha1. As the N rate went up, the oil and starch content of the grain decreased, whereas the protein content and the protein, starch, and oil yields increased, reaching their maximum at the N rate corresponding to 179.0 kg N.ha~. The potential ethanol yield varied between 616.2 and 7,035.1 L-ha1 depending on the method of conversion of the starch into ethanol, the year and the N rate （P 〈 0.0001）. The negative correlation between N fertilization rate and starch content suggested that when farmers add too much N to their soil to increase grain yield, they reduce the starch content in those grains, and consequently the conversion into bioethanol. Therefore, for biofuel production to be beneficial for both farmers and the power plant owners, an agreement needs to be made with regard to the use of fertilizers.
