Tremella Polysaccharides attenuated sepsis through inhibiting abnormal CD4+CD25 high regulatory T cells in mice / 中国免疫学杂志

Objective:To determine the effects of TPS on peripheral blood Tregs in sepsis mouse induced by burn plus P.aeruginosa infection.Methods: The experimental mice were separated into five groups randomly ,including sham burn group ,burn plus P.aeruginosa infection group ,burn plus P.aeruginosa infection with TPS (50,100,200 mg/kg) treatment group.Peripheral blood Tregs were isolated with Magnetic Microbeads and cultured in vitro from the day after burn (PBD0) to 4 days after burn(PBD4).IL-10, IFN-γ,IL-4 levels in Tregs culture supernatants were determined by sandwich enzyme-linked immunosorbent assays ( ELISA ) . Purification of CD4+CD25high Tregs and CD4+T cells in C57BL/6 mice were administrated by magnetic beads sorting .Tregs and CD4+T cells were cultured in vitro after joining TPS to without TPS cells as a control .The phenotypes of Tregs were analyzed by flow cytometry , and cytokines were measured by ELISA .Results:Vis-a-vis the results of the untreated group ,TPS could markedly decrease IL-4 and IL-10 secretion level and significantly increase the secretion of IFN-γ,and the secretion of IL-10 level and concentration of TPS dose effect.Vis-a-vis the results of the untreated group ,in vitro experiment ,without stimulation of TPS ,CD4+T cell proliferation and IFN-γwere significantly reduced ( P<0.05 ) and IL-4 levels increased significantly;CD4+T cell proliferation and IFN-γwere significantly increased and IL-4 levels were significantly reduced in the group of TPS with antibody-1;there was no significant difference in CD 4+T cell proliferation and the levels of IFN-γand IL-4 in the group of TPS with antibody-2.Conclusion:TPS could inhibit the abnormal ac-tivities of CD4+CD25highTregs in burn with P.aeruginosa infection mice,at least in part via inhibiting IL-10 secretion,and trigger a shift of Th2 to Th1 with activation of CD4+T cells in burn with P.aeruginosa infection mice.

Blind Separation of Superimposed Moving Images Using Image Statistics.

We address the problem of blind separation of multiple source layers from their linear mixtures with unknown mixing coefficients and unknown layer motions. Such mixtures can occur when one takes photos through a transparent medium, like a window glass, and the camera or the medium moves between snapshots. To understand how to achieve correct separation, we study the statistics of natural images in the Labelme data set. We not only confirm the well-known sparsity of image gradients, but also discover new joint behavior patterns of image gradients. Based on these statistical properties, we develop a sparse blind separation algorithm to estimate both layer motions and linear mixing coefficients and then recover all layers. This method can handle general parameterized motions, including translations, scalings, rotations, and other transformations. In addition, the number of layers is automatically identified, and all layers can be recovered, even in the underdetermined case where mixtures are fewer than layers. The effectiveness of this technology is shown in experiments on both simulated and real superimposed images.

Blind source separation for fMRI signals using a new independent component analysis algorithm and principal component analysis / 生物医学工程学杂志

The application of independent component analysis (ICA) to the functional magnetic resonance imaging (fMRI) data can separate many independent sources. But in the processing there are two difficulties: (1) the data of the fMRI is usually on a large scale, so the computing is time-consuming; (2) we cannot avoid the errors for too heavy computational load, this brings many troubles. Thus we think of reducing the data. In this article we used the standard information theoretic methods to estimate the number of the sources and used the principal component analysis (PCA) to reduce the data. By this process, we estimated the number of the sources and reduced the data successfully; Then we applied the ICA algorithm to the reduced fMRI data; this method raised the speed of operation. After application of the new ICA algorithm and another algorithm (FastICA) to the fMRI data, a comparison was made. The results show that the new algorithm can separate the fMRI data fast and effectively and it is superior to the FastICA on the accuracy of estimating the temporal dynamics of activations.