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Yang Liu

Researcher at Nanyang Technological University

Publications -  2464
Citations -  49586

Yang Liu is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 82, co-authored 1695 publications receiving 33657 citations. Previous affiliations of Yang Liu include Zhejiang University & Yangzhou University.

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CD24 and Siglec-10 Selectively Repress Tissue Damage-Induced Immune Responses

TL;DR: The results reveal that the CD24–Siglec G pathway protects the host against a lethal response to pathological cell death and discriminates danger- versus pathogen-associated molecular patterns.
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TSC–mTOR maintains quiescence and function of hematopoietic stem cells by repressing mitochondrial biogenesis and reactive oxygen species

TL;DR: It is demonstrated that Tsc1 deletion in the HSCs drives them from quiescence into rapid cycling, with increased mitochondrial biogenesis and elevated levels of reactive oxygen species (ROS), which may explain the well-documented association between quiescent and the “stemness” of H SCs.
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Direct visualization of current-induced spin accumulation in topological insulators.

TL;DR: Spatial imaging of current-induced spin accumulation at the edges of Bi2Se3 and BiSbTeSe2 topological insulators as well as Pt by a scanning photovoltage microscope at room temperature points towards a better understanding of the interaction between spins and circularly polarized light.
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The Therapeutic Effect of Anti-HER2/neu Antibody Depends on Both Innate and Adaptive Immunity

TL;DR: It is demonstrated that the mechanisms of tumor regression by anti-HER2/neu antibody therapy also require the adaptive immune response, and the addition of chemotherapeutic drugs, although capable of enhancing the reduction of tumor burden, could abrogate antibody-initiated immunity leading to decreased resistance to rechallenge or earlier relapse.
Posted Content

graph2vec: Learning Distributed Representations of Graphs

TL;DR: This work proposes a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs that achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.