Polymorphism in exon 1 of adiponectin gene and its association with water holding capacity, IMF and abdominal fat in duck

The objective of the present study was to investigate the effect of polymorphism in adiponectin gene on meat quality traits, serum total cholesterol, serum triglyceride and abdominal fat of 170 individuals from Cherry Valley duck (CV), Jinding duck (JD) and Hybrid duck (CV × JD) (HB) populations. PCR-SSCP technique was developed to analyze a 230 bp region of the adiponectin gene exon 1. Three genotypes (CC, CD and DD), which were the products of two alleles (C and D) were observed. Alignment sequences’ results showed that four SNPs (C86T, C104T, C146T and C155T) were found and all of those nucleotide variations were nonsense mutations. Association analysis indicated that all of these traits had significant population effects except meat colour (P < 0.05) and then the birds with homozygote (CC) had significant lower than homozygotes (DD) for IMF, water holding capacity and abdominal fat (P < 0.05). The research suggested that genotype DD may be an advantage genotype for fat deposition in duck. The adiponectin gene exon 1 polymorphism could be used in marker assistant selection (MAS) as a genetic marker for the birds’ fat deposition.Key words: Duck, adiponectin gene, polymorphism, meat quality, fatness

Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks

Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their perturbation values while fixing the pasting position or manipulating the position while fixing the patch's content. This reveals that the positions and perturbations are both important to the adversarial attack. For that, in this paper, we propose a novel method to simultaneously optimize the position and perturbation for an adversarial patch, and thus obtain a high attack success rate in the black-box setting. Technically, we regard the patch's position, the pre-designed hyper-parameters to determine the patch's perturbations as the variables, and utilize the reinforcement learning framework to simultaneously solve for the optimal solution based on the rewards obtained from the target model with a small number of queries. Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency. Besides, experiments on the commercial FR service and physical environments confirm its practical application value. We also extend our method to the traffic sign recognition task to verify its generalization ability.Comment: Accepted by TPAMI 202