Nucleotide sequence diversity of HLA class II genes in Australian Aborigines and populations of Asia-Oceania

The aim of this thesis was to investigate nucleotide sequence diversity of HLA class II genes in Australian Aborigines and indigenous peoples of Asia-Oceania. Nineteen study populations represented eight major ethnic groups including Australian Aborigines, Papua New Guinean highlanders, coastal Melanesians, Polynesians, Micronesians, Javanese, southern and northern Chinese, and a minority group from northwestern China. Using PCR-based technologies, the nucleotide sequence polymorphism in exon 2 DRB1, DRB3, DRB5, DQA1 and DQB1 genes was examined in all these populations. The DPB1 exon 2 polymorphism was examined in Australian Aborigines and a Chinese population. Six novel HLA class II alleles including four DRB1, one DRB5 and one DPBl were discovered in this study by the occurrence of unusual hybridization patterns in the PCR-SSO typing procedure and were confirmed by DNA Sequencing. These new alleles, DRB1*0412, 1408, 1409, 1410, DRB5*0203 and DBP1*2201 have been recognized by the WHO Nomenclature Committee. The nucleotide sequences and the deduced amino acid sequences of the novel class II alleles indicated that multiple molecular mechanisms were involved in generating these alleles including point mutation and hypermutational events of segmental transfer and intra-exonic recombination. In two cases (DRB1*0412 and DRB1*1410), hypermutational events have created unique peptide binding sites which are drastically different from all their putative progenitor molecules. Five of the six novel alleles were found in Australian Aborigines and four novel DRB1 alleles were detected in 45% of the Aboriginal individuals tested. PCR-SSO typing revealed some HLA class II polymorphisms previously difficult or impossible to detect with more traditional typing techniques. Remarkable differences in the V class II HLA allele frequency distributions, especially in the subtypes of major DR antigen groups, were observed between the study populations. Australian Aborigines showed the most divergent class II HLA profile; most of their DRB1 alleles did not overlap with other study populations. PNG highlanders and Javanese were highly homogeneous with quite restricted class II HLA distributions. Other Oceanic populations of Polynesians, Micronesians and coastal Melanesians were each characterized with unique class II HLA distribution but shared common features which indicated their historical ties. Distinctive HLA distributions were observed between Chinese populations from southern and northern China, while the minority group from northwestern China demonstrated a mixed ancestry of both Caucasoids and Orientals. Further information came from the analysis of HLA-DR, -DQ haplotypes. A total of 80 three-locus or four-locus DR-DQ combinations including 16 DR2-related, 12 DR4-related, 11 DR5- related, and 24 DR6-related haplotypes were inferred from the study populations. Haplotype frequencies were used to calculate genetic distances between these populations and to reconstruct population phylogeny, which proved a sensitive indicator of population affinities. The unusual linkage relationships detected in the study populations also had important implications for the understanding of MHC evolution. Knowledge of the nucleotide sequence polymorphism of HLA class II genes in general populations has fundamental importance in HLA-related clinical investigations. The apparent lack of susceptible alleles in the HLA gene pool of native Australians and Pacific islanders, or the high frequency of protective alleles, might partly explain the extremely low incidence of autoimmune diseases in these populations

Generating Multiple Diverse Responses for Short-Text Conversation

Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-of-the-art generative models.Comment: Accepted for publication at AAAI 201

A Deep-learning Real-time Bias Correction Method for Significant Wave Height Forecasts in the Western North Pacific

Significant wave height is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the ECMWF-IFS. This method was built on the trajectory gated recurrent unit deep neural network,and it conducts real-time rolling correction for the 0-240h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972~46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794~38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.Comment: 21 page