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The present study aimed to analyse the frequencies of human leukocyte antigen HLA-ABCDQB1 and HLA-DRB1 alleles and haplotypes in a subset of 3,732 Han population from Hubei of China. All samples were typed in the HLA-ABCDQB1 and HLA-DRB1 loci using the sequence-based typing method; subsequently, the HLA polymorphisms were analysed. A total of 47 HLA-A, 89 HLA-B, 43 HLA-C, 49 HLA-DRB1 and 24 HLA-DQB1 alleles were identified in the Hubei Han population. The top three most frequent alleles in the HLA-ABCDQB1 and HLA-DRB1 were A*11:01 (0.2617), A*24:02 (0.1590), A*02:07 (0.1281); B*46:01 (0.1502), B*40:01 (0.1409) and B*58:01 (0.0616); C*01:02 (0.2023), C*07:02 (0.1691) and C*03:04 (0.1175); and DQB1*03:01 (0.2000), DQB1*03:03 (0.1900), DQB1*06:01 (0.1187); DRB1*09:01 (0.1790), DRB1*15:01 (0.1062) and DRB1*12:02 (0.0841), respectively. Meanwhile, the three most frequent two-loci haplotypes were A*02:07-C*01:02 (0.0929), B*46:01-C*01:02 (0.1366) and DQB1*03:03-DRB1*09:01 (0.1766). The three most frequent three-loci haplotypes were A*02:07-B*46:01-C*01:02 (0.0883), B*46:01-DQB1*03:03-DRB1*09:01 (0.0808) and C*01:02-DQB1*03:03-DRB1*09:01 (0.0837). The three most frequent four-loci haplotypes were A*02:07-B*46:01-C*01:02-DQB1*03:03 (0.0494), B*46:01-DRB1*09:01-C*01:02-DQB1*03:03 (0.0729) and A*02:07-B*46:01-DQB1*03:03-DRB1*09:01 (0.0501). The most frequent five-loci haplotype was A*02:07-B*46:01-C*01:02-DQB1*03:03-DRB1*09:01 (0.0487). Heat maps and multiple correspondence analysis based on the frequencies of HLA specificity indicated that the Hubei Han population might be described into Southern Chinese populations. Our results lay a certain foundation for future population studies, disease association studies and donor recruitment strategies.

The segmentation of color images as a preprocessing to recognize objects is an important computer vision technique for robotic environment modeling. Linking image sequences to identify all the segments belonging to the same objects is a crucial and challenging problem, especially given the large volumes of image data. In this paper, we propose an aggregation-based fast K-medoids clustering algorithm as a solution for an efficient as well as reliable image segmentation and object recognition problem.

HLA-B*58:118 differs from HLA-B*58:01:01:01 by one nucleotide exchange at position 556(G>A) with an amino exchange.

HLA-B*46:01:21 differs from HLA-B*46:01:01 by one nucleotide exchange at position 834(G>A) with no amino exchange.

HLA-B*40:317 differs from HLA-A*40:06:01:01 by one nucleotide exchange at position 344(G>T) with an amino exchange.