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Xiujuan Tian,Wenjing Qi,Hongyu Chen,Xianlu Zeng,Liping Han,Donghui Mi 한국통합생물학회 2016 Animal cells and systems Vol.20 No.5
In pre-initiation complexes, RNA helicase A interacts with β-actin and acts as a bridging factor linking nuclear actin with RNA polymerase II (Pol II). In addition, β-actin participates in Pol IIdependent transcription elongation by interacting with the positive transcription elongation factor Cdk9. However, many relationships between β-actin and Pol II remain to be identified. In an interleukin 6 (IL-6)-induced p21 expression model, we demonstrated that β-actin knockdown reduced p21 expression. Immunofluorescence analysis showed that the colocalization of β-actin and Pol II increased significantly in cells treated with IL-6. It is known that the Rpb5, Rpb6 and Rpb7 subunits are located at the surface of the enzyme. We next constructed recombinant pcDNA-HA-Rpb5, pcDNA-HA-Rpb6 and pcDNA-HA-Rpb7 plasmids and expressed the three polymerase II subunits in HepG2 cells. We found that β-actin could be immunoprecipitated with HA-Rpb5 and HA-Rpb7. A Glutathione-S-transferase pull-down assay revealed that β-actin was associated with Rpb5 and Rpb7 in vitro. Furthermore, overexpression of Rpb5 and Rpb7 in cells reduced p21 expression significantly, suggesting that Rpb5 and Rpb7 competitively interact with β-actin. This study shows that β-actin associates with Pol II subunits through direct proteinprotein interactions and provides fundamental insight into Pol II transcriptional regulation.
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Dynamic Gesture Recognition based on Image Sequence
Lihua Tian,Liguo Han,Xiujuan Gu 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.12
This paper proposed an algorithm for 3D hands tracking on the learned hierarchical latent variable space, which employs a Hierarchical Gaussian Process Latent Variable Model(HGPLVM) to learn the hierarchical latent space of hands motion and the nonlinear mapping from the hierarchical latent space to the pose space simultaneously. Nonlinear mappings from the hierarchical latent space to the space of hand images are constructed using radial basis function interpolation method. With these mappings, particles can be projected into hand images and measured in the image space directly. Particle filters with fewer particles are used to track the hand on the learned hierarchical low-dimensional space. Then the Hierarchical Conditional Random Field, which can capture extrinsic class dynamics and learn the relationship between motions of hand parts and different hand gestures simultaneously, is presented to model the continuous hand gestures. Experimental results show that our proposed method can track articulated hand robustly and approving recognition performance has also been achieved on the user-defined hand gesture dataset.
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