Modified 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide(EDC)method was employed to synthesize the artificial antigen of norfloxacin(NOR),and New Zealand rabbits were used to produce anti-NOR polyclonal antibody(pAb).Ba...Modified 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide(EDC)method was employed to synthesize the artificial antigen of norfloxacin(NOR),and New Zealand rabbits were used to produce anti-NOR polyclonal antibody(pAb).Based on the checkerboard titration,an indirect competitive enzyme-linked immunosorbent assay(icELISA) standard curve was established.This assay was sensitive and had a working range from 0.12 to 68.40 ng/ml,with the half maximal inhibitory concentration(IC50)and limit of detection(LOD)values of 2.7 ng/ml and 0.06 ng/ml,respectively.The produced pAb exhibited high cross-reactivity to fluoroquinolones(FQs)tested,and the IC50 values to enoxacin,ciprofloxacin,and pefloxacin were 3.1,3.4,and 4.1 ng/ml,respectively.It also indicated that the concentrations of NaOH and methanol in assay buffer should not be higher than 10%and 30%.When spiked in milk at 5,20,and 50 ng/ml,the recoveries for NOR,enoxacin,ciprofloxacin,and pefloxacin ranged 90.5%-98.0%,84.0%-95.2%,94.0%-106.0%,and 89.5%-100.0%,respectively.The results suggest that this class-specific pAb-based icELISA could be utilized for the primary screening of FQ residues in animal-original products.展开更多
To achieve online automatic classification of product is a great need of e-commerce de-velopment. By analyzing the characteristics of product images, we proposed a fast supervised image classifier which is based on cl...To achieve online automatic classification of product is a great need of e-commerce de-velopment. By analyzing the characteristics of product images, we proposed a fast supervised image classifier which is based on class-specific Pyramid Histogram Of Words (PHOW) descriptor and Im-age-to-Class distance (PHOW/I2C). In the training phase, the local features are densely sampled and represented as soft-voting PHOW descriptors, and then the class-specific descriptors are built with the means and variances of distribution of each visual word in each labelled class. For online testing, the normalized chi-square distance is calculated between the descriptor of query image and each class-specific descriptor. The class label corresponding to the least I2C distance is taken as the final winner. Experiments demonstrate the effectiveness and quickness of our method in the tasks of product clas-sification.展开更多
Existing explanation methods for Convolutional Neural Networks(CNNs)lack the pixel-level visualization explanations to generate the reliable fine-grained decision features.Since there are inconsistencies between the e...Existing explanation methods for Convolutional Neural Networks(CNNs)lack the pixel-level visualization explanations to generate the reliable fine-grained decision features.Since there are inconsistencies between the explanation and the actual behavior of the model to be interpreted,we propose a Fine-Grained Visual Explanation for CNN,namely F-GVE,which produces a fine-grained explanation with higher consistency to the decision of the original model.The exact backward class-specific gradients with respect to the input image is obtained to highlight the object-related pixels the model used to make prediction.In addition,for better visualization and less noise,F-GVE selects an appropriate threshold to filter the gradient during the calculation and the explanation map is obtained by element-wise multiplying the gradient and the input image to show fine-grained classification decision features.Experimental results demonstrate that F-GVE has good visual performances and highlights the importance of fine-grained decision features.Moreover,the faithfulness of the explanation in this paper is high and it is effective and practical on troubleshooting and debugging detection.展开更多
基金Project supported by the Henan Innovation Project for University Prominent Research Talents(No.2010HASTIT026)the Key Scientific & Technological Project of Education Department in Henan Province of China(No.2011A230003)
文摘Modified 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide(EDC)method was employed to synthesize the artificial antigen of norfloxacin(NOR),and New Zealand rabbits were used to produce anti-NOR polyclonal antibody(pAb).Based on the checkerboard titration,an indirect competitive enzyme-linked immunosorbent assay(icELISA) standard curve was established.This assay was sensitive and had a working range from 0.12 to 68.40 ng/ml,with the half maximal inhibitory concentration(IC50)and limit of detection(LOD)values of 2.7 ng/ml and 0.06 ng/ml,respectively.The produced pAb exhibited high cross-reactivity to fluoroquinolones(FQs)tested,and the IC50 values to enoxacin,ciprofloxacin,and pefloxacin were 3.1,3.4,and 4.1 ng/ml,respectively.It also indicated that the concentrations of NaOH and methanol in assay buffer should not be higher than 10%and 30%.When spiked in milk at 5,20,and 50 ng/ml,the recoveries for NOR,enoxacin,ciprofloxacin,and pefloxacin ranged 90.5%-98.0%,84.0%-95.2%,94.0%-106.0%,and 89.5%-100.0%,respectively.The results suggest that this class-specific pAb-based icELISA could be utilized for the primary screening of FQ residues in animal-original products.
基金Supported by the Major Funded Project of National Natural Science Foundation of China (No. 70890083)
文摘To achieve online automatic classification of product is a great need of e-commerce de-velopment. By analyzing the characteristics of product images, we proposed a fast supervised image classifier which is based on class-specific Pyramid Histogram Of Words (PHOW) descriptor and Im-age-to-Class distance (PHOW/I2C). In the training phase, the local features are densely sampled and represented as soft-voting PHOW descriptors, and then the class-specific descriptors are built with the means and variances of distribution of each visual word in each labelled class. For online testing, the normalized chi-square distance is calculated between the descriptor of query image and each class-specific descriptor. The class label corresponding to the least I2C distance is taken as the final winner. Experiments demonstrate the effectiveness and quickness of our method in the tasks of product clas-sification.
基金This work was partially supported by Beijing Natural Science Foundation(No.4222038)by Open Research Project of the State Key Laboratory of Media Convergence and Communication(Communication University of China),by the National Key RD Program of China(No.2021YFF0307600)and by Fundamental Research Funds for the Central Universities.
文摘Existing explanation methods for Convolutional Neural Networks(CNNs)lack the pixel-level visualization explanations to generate the reliable fine-grained decision features.Since there are inconsistencies between the explanation and the actual behavior of the model to be interpreted,we propose a Fine-Grained Visual Explanation for CNN,namely F-GVE,which produces a fine-grained explanation with higher consistency to the decision of the original model.The exact backward class-specific gradients with respect to the input image is obtained to highlight the object-related pixels the model used to make prediction.In addition,for better visualization and less noise,F-GVE selects an appropriate threshold to filter the gradient during the calculation and the explanation map is obtained by element-wise multiplying the gradient and the input image to show fine-grained classification decision features.Experimental results demonstrate that F-GVE has good visual performances and highlights the importance of fine-grained decision features.Moreover,the faithfulness of the explanation in this paper is high and it is effective and practical on troubleshooting and debugging detection.
