Flaxseed cake contains cyanogenic glucosides, which can be metabolized into hydrocyanic acid in an animal's body, leading to asphyxia poisoning in cells. Beta-glucosidase is highly efficient in degrading cyanogeni...Flaxseed cake contains cyanogenic glucosides, which can be metabolized into hydrocyanic acid in an animal's body, leading to asphyxia poisoning in cells. Beta-glucosidase is highly efficient in degrading cyanogenic glucosides. The Cattle may have b-glucosidase-producing strains in the intestinal tract after eating small amounts of flaxseed cake for a long time. This study aimed to isolate of a strain from cow dung that produces b-glucosidase with high activity and can significantly reduce the amount of cyanogenic glucosides. We used cow dung as the microflora source and an esculin agar as the selective medium. After screening with 0.05% esculin and 0.01% ferric citrate, we isolated 5 strains producing high amounts of b-glucosidase. In vitro flaxseed cake fermentation was fermented by these 5 strains, in which the strain M-2 exerted the best effect(P < 0.05). The strain M-2 was identified as Lichtheimia ramosa and used as the fermentation strain to optimize the fermentation parameters by a single factor analysis and orthogonal experimental design. The optimum condition was as follows: inoculum size3%, water content 60%, time 144 h, and temperature 32℃. Under this condition, the removal rate of cyanogenic glucosides reached 89%, and crude protein increment reached 44%. These results provided a theoretical basis for the removal of cyanogenic glucosides in flaxseed and the comprehensive utilization of flaxseed cake.展开更多
In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techni...In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.展开更多
Masked autoencoders(MAEs)have emerged as a powerful self-supervised approach for point cloud analysis.Nevertheless,existing methods often separately focus on global structures or multi-scale features,ignoring their co...Masked autoencoders(MAEs)have emerged as a powerful self-supervised approach for point cloud analysis.Nevertheless,existing methods often separately focus on global structures or multi-scale features,ignoring their complementary potential.In this paper,we propose a novel dual-Siamese masked autoencoder(DS-MAE)framework that explores integrating global and hierarchical feature learning in a unified architecture for point cloud analysis.In particular,we introduce a consistent dual-branch patch embedding strategy to partition the point cloud into patches using shared group centers,ensuring both global and hierarchical branches process point patches centered at the same spatial locations.Each branch employs dual-branch Siamese encoders to process original and augmented point patches,learning representations that capture both local details and global context.In addition,we have designed cross-attention Siamese decoders to reconstruct masked point patches and align features both within and between branches with crossattention mechanisms.Comprehensive experiments demonstrate our method consistently achieves superior results to prior methods.Code is available at https://github.com/shaoandy1211/DS-MAE.git.展开更多
基金supported by Jiangsu Science and Technology Major Project (BA2016036)Lanzhou Science and Technology Funds (2015-3-81)Gansu Science and Technology Major Project (17ZD2FA009)
文摘Flaxseed cake contains cyanogenic glucosides, which can be metabolized into hydrocyanic acid in an animal's body, leading to asphyxia poisoning in cells. Beta-glucosidase is highly efficient in degrading cyanogenic glucosides. The Cattle may have b-glucosidase-producing strains in the intestinal tract after eating small amounts of flaxseed cake for a long time. This study aimed to isolate of a strain from cow dung that produces b-glucosidase with high activity and can significantly reduce the amount of cyanogenic glucosides. We used cow dung as the microflora source and an esculin agar as the selective medium. After screening with 0.05% esculin and 0.01% ferric citrate, we isolated 5 strains producing high amounts of b-glucosidase. In vitro flaxseed cake fermentation was fermented by these 5 strains, in which the strain M-2 exerted the best effect(P < 0.05). The strain M-2 was identified as Lichtheimia ramosa and used as the fermentation strain to optimize the fermentation parameters by a single factor analysis and orthogonal experimental design. The optimum condition was as follows: inoculum size3%, water content 60%, time 144 h, and temperature 32℃. Under this condition, the removal rate of cyanogenic glucosides reached 89%, and crude protein increment reached 44%. These results provided a theoretical basis for the removal of cyanogenic glucosides in flaxseed and the comprehensive utilization of flaxseed cake.
文摘In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.
文摘Masked autoencoders(MAEs)have emerged as a powerful self-supervised approach for point cloud analysis.Nevertheless,existing methods often separately focus on global structures or multi-scale features,ignoring their complementary potential.In this paper,we propose a novel dual-Siamese masked autoencoder(DS-MAE)framework that explores integrating global and hierarchical feature learning in a unified architecture for point cloud analysis.In particular,we introduce a consistent dual-branch patch embedding strategy to partition the point cloud into patches using shared group centers,ensuring both global and hierarchical branches process point patches centered at the same spatial locations.Each branch employs dual-branch Siamese encoders to process original and augmented point patches,learning representations that capture both local details and global context.In addition,we have designed cross-attention Siamese decoders to reconstruct masked point patches and align features both within and between branches with crossattention mechanisms.Comprehensive experiments demonstrate our method consistently achieves superior results to prior methods.Code is available at https://github.com/shaoandy1211/DS-MAE.git.
