The Genetic Associations and Epistatic Effects of the CCR5 Promoter and CCR2-V64I Polymorphisms on Susceptibility to HIV-1 Infection in a Northern Han Chinese Population

The outcome of human immunodeficiency virus (HIV)-1 infection and course to AIDS are variable among individuals. Both chemokine receptor 5 (CCR5) and CCR2 gene polymorphisms play essential roles in the susceptibility of HIV-1 infection. To investigate the main and epistatic effects of the CCR5 promoter and CCR2-V64I polymorphisms on HIV-1 infection in the Northern Han Chinese, subjects of 91 HIV-1-infected patients and 91 health controls were recruited. Single-nucleotide polymorphisms (SNPs) in the CCR5 promoter region and CCR2-V64I variants were genotyped. In the single-locus analysis, CCR5 58755-G and CCR5 59653-T alleles were significantly associated with HIV-1 infection (odds ratio [OR]=0.529, 95% confidence interval [CI]: 0.295-0.948; OR=1.710, 95% CI: 1.039-2.814). After adjustment with age and gender, subjects with the CCR5 59653-CT genotype showed the increased risk of HIV-1 infection compared with those with the wild-type CC genotype (adjusted OR=2.502; 95% CI: 1.332-4.698). No positive association was observed in other SNPs. Haplotype-based association analysis revealed that the haplotype TATGC was associated with the susceptibility to HIV-1 infection (p=0.003). Besides, we found the significant epistatic effects between the CCR5 58755-A/G and CCR5 59029-A/G polymorphisms associated with the lower risk of HIV-1 infection. In addition, we also identified the best three-factor interaction model, including the CCR5 58755-A/G, 59029-A/G, and CCR2-V64I polymorphisms, indicating that there were also strong gene-gene interactions between the CCR5 promoter and CCR2 polymorphisms on the susceptibility of HIV-1 infection. These findings contribute to understanding the genetic mechanism for the susceptibility of HIV-1 infection in Northern Han Chinese

Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Summarizing knowledge from animals and human beings inspires robotic innovations. In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments. Inspired by large pre-trained models witnessed with impressive performance in language and image understanding, we introduce the power of advanced deep generative models to produce motor control signals stimulating legged robots to act like real animals. Unlike conventional controllers and end-to-end RL methods that are task-specific, we propose to pre-train generative models over animal motion datasets to preserve expressive knowledge of animal behavior. The pre-trained model holds sufficient primitive-level knowledge yet is environment-agnostic. It is then reused for a successive stage of learning to align with the environments by traversing a number of challenging obstacles that are rarely considered in previous approaches, including creeping through narrow spaces, jumping over hurdles, freerunning over scattered blocks, etc. Finally, a task-specific controller is trained to solve complex downstream tasks by reusing the knowledge from previous stages. Enriching the knowledge regarding each stage does not affect the usage of other levels of knowledge. This flexible framework offers the possibility of continual knowledge accumulation at different levels. We successfully apply the trained multi-level controllers to the MAX robot, a quadrupedal robot developed in-house, to mimic animals, traverse complex obstacles, and play in a designed challenging multi-agent Chase Tag Game, where lifelike agility and strategy emerge on the robots. The present research pushes the frontier of robot control with new insights on reusing multi-level pre-trained knowledge and solving highly complex downstream tasks in the real world

A New Leaf Blight Disease Caused by <i>Alternaria jacinthicola</i> on Banana in China

A leaf blight disease with an incidence level of about 50% was found on Robusta banana in Guangdong province of China in September 2020. The early symptom appeared as pale gray to black brown, irregular, small, necrotic lesions mainly on the top 3–5 leaves. Severely infected leaves were withered and necrotic. Two representative fungus strains, strain L1 and strain L2, were isolated from affected banana leaves, and morphological and molecular identification analysis confirmed that the two fungi were both Alternaria jacinthicola. Many Alternaria species have been reported to cause wilting, decay, leaf blight and leaf spots on plants and lead to serious economic losses in their production, including A. alternata, causing leaf blight and leaf sport diseases on banana. The Koch’s postulates of A. jacinthicola causing the leaf blight disease was further fulfilled, which confirmed that it is the causal agent of this disease. To our knowledge, this is the first report of A. jacinthicola causing leaf blight on banana in China

Genome-wide association analysis identifies three new risk loci for gout arthritis in Han Chinese

Gout is one of the most common types of inflammatory arthritis, caused by the deposition of monosodium urate crystals in and around the joints. Previous genome-wide association studies (GWASs) have identified many genetic loci associated with raised serum urate concentrations. However, hyperuricemia alone is not sufficient for the development of gout arthritis. Here we conduct a multistage GWAS in Han Chinese using 4,275 male gout patients and 6,272 normal male controls (1,255 cases and 1,848 controls were genome-wide genotyped), with an additional 1,644 hyperuricemic controls. We discover three new risk loci, 17q23.2 (rs11653176, P=1.36 × 10(-13), BCAS3), 9p24.2 (rs12236871, P=1.48 × 10(-10), RFX3) and 11p15.5 (rs179785, P=1.28 × 10(-8), KCNQ1), which contain inflammatory candidate genes. Our results suggest that these loci are most likely related to the progression from hyperuricemia to inflammatory gout, which will provide new insights into the pathogenesis of gout arthritis