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A number of genetic loci have been proposed to be associated with persistent hepatitis B virus (HBV) infection. This study aimed to evaluate the association and interaction of susceptible genes with HBV persistence in a Chinese population. A total of 17 polymorphisms in 9 candidate genes were studied in 361 Chinese chronic hepatitis B patients and 304 patients who recovered spontaneously. Distributions of susceptible polymorphisms were examined in healthy Chinese and Caucasian populations. Gene-gene interactions were tested by the multifactor dimensionality reduction (MDR) method. The TNF -308 G/G genotype and G allele, IL-10RB codon 47 A allele, and MCP-1 -2518 G/G genotype and G allele were more frequent in patients than controls (P < 0.01, after multiple corrections Pc < 0.05), while the frequencies of TNF -308 A/G genotype and IL-10 -592 A/A genotype were significantly higher in controls than in the patient group (Pc < 0.05). The frequencies of the risk allele MCP-1 -2518 G and CTLA4 6230 G were much higher in Chinese than in the Caucasian groups (P < 0.001). An interaction between CCR5 -2459, TNFA -863, IL-10RB codon 47, and MCP-1 -2518 was detected by MDR (P = 0.001). The results indicate that genetic determinants may affect the outcome of HBV infection in both independent and synergic manners. J. Med. Virol. 82:371-378, 2010. (c) 2010 Wiley-Liss, Inc.

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We performed Killer cell immunoglobulin-like receptor (KIR) genotyping on 1271 individuals of Chinese Han origin including 102 families and 965 unrelated individuals. The families (with one child and both parents) were subjected for haplotype analysis. Forty-one different genotypes were identified. The frequencies of the KIR genotypes found in the family panel were confirmed by those found in the unrelated panel. The family study showed segregation of one A haplotype and at least 15 unique B haplotypes. The most commonly observed haplotypes in group B were B1, B2, and B3, present at a frequency of 10.05%, 6.62%, and 4.90%, respectively. On the basis of the combination of KIR genes, six centromeric and seven telomeric gene motifs have been identified. Motif cB02 was the most frequent haplotype B specific centromeric segment while tB01 was the most frequent haplotype B specific telomeric segment. The distinct distribution of KIR haplotypes in each population may reflect the history of directional and balancing selection of different races. The gene combinations of group A and B1/B2/B3 haplotype were the most frequent genotypes named as Bx1, Bx2, and Bx3, present at a frequency of 13.72%, 7.35%, and 4.41% in the family panel, and at a frequency of 15.86%, 10.15%, and 5.80% in the unrelated panel, respectively. Overall, this study showed the diversity of KIR haplotypes and genotypes in Chinese Han population and developed a criterion for distinguish KIR haplotypes/genotypes for the population. KIR genotyping and haplotype analysis should be useful for selection of the most optimum donor grafts with favorable KIR gene content for transplants.

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

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