Inhibition of NF-kB 1 (NF-kBp50) by RNA interference in chicken macrophage HD11 cell line challenged with Salmonellaenteritidis

The NF-kB pathway plays an important role in regulating the immunity response in animals. In this study, small interfering RNAs (siRNA) were used to specifically inhibit NF-kB 1 expression and to elucidate the role of NF-kB in the signal transduction pathway of the Salmonella challenge in the chicken HD11 cell line. The cells were transfected with either NF-kB 1 siRNA, glyceraldehyde 3-phosphate dehydrogenase siRNA (positive control) or the negative control siRNA for 24 h, followed by Salmonella enteritidis (SE) challenge or non-challenge for 1 h and 4 h. Eight candidate genes related to the signal pathway of SE challenge were selected to examine the effect of NF-kB 1 inhibition on their expressions by mRNA quantification. The results showed that, with a 36% inhibition of NF-kB 1 expression, gene expression of both Toll-like receptor (TLR) 4 and interleukin (IL)-6 was consistently and significantly increased at both 1 h and 4 h following SE challenge, whereas the gene expression of MyD88 and IL-1β was increased at 1 h and 4 h, respectively. These findings suggest a likely inhibitory regulation by NF-kB 1, and could lay the foundation for studying the gene network of the innate immune response of SE infection in chickens

Spectral methods for computer vision problems : micro and macro-prudential issues

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Image segmentation using commute times

This paper exploits the properties of the commute time to develop a graphspectral method for image segmentation. Our starting point is the lazy random walk on the graph, which is determined by the heat-kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the Laplacian spectrum using the discrete Green’s function. We explore the application of the commute time for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix.

Clustering and Embedding using Commute Times

This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding, and explores its applications to image segmentation and multi-body motion tracking. Our starting point is the lazy random walk on the graph, which is determined by the heatkernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterize the random walk using the commute time (i.e. the expected time taken for a random walk to travel between two nodes and return) and show how this quantity may be computed from the Laplacian spectrum using the discrete Green’s function. Our motivation is that the commute time can be anticipated to be a more robust measure of the proximity of data than the raw proximity matrix. In this paper, we explore two applications of the commute time. The first is to develop a method for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix. We show that our commute time segmentation method has the property of enhancing the intra-group coherence while weakening inter-group coherence and is superior to the normalized cut. The second application is to develop a robust multi-body motion tracking method using an embedding based on the commute time. Our embedding procedure preserves commute time, and is closely akin to kernel PCA, the Laplacian eigenmap and the diffusion map. We illustrate the results both on synthetic image sequences and real world video sequences, and compare our results with several alternative methods

Graph Matching using Commute Time Spanning Trees

This paper exploits the properties of the commute time for the purposes of graph matching. Our starting point is the random walk on the graph, which is determined by the heat-kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the Laplacian spectrum using the discrete Green’s function. We use the commute-time to locate the minimum spanning tree of the graph. The spanning trees located using commute time prove to be stable to structural variations. We match the graphs by applying a tree-matching method to the spanning trees. We experiment with the method on synthetic and real-world image data, where it proves to be effective.

Robust Multi-body Motion Tracking using Commute Time Clustering

Abstract. The presence of noise renders the classical factorization method almost impractical for real-world multi-body motion tracking problems. The main problem stems from the effect of noise on the shape interaction matrix, which looses its block-diagonal structure and as a result the assignment of elements to objects becomes difficult. The aim in this paper is to overcome this problem using graph-spectral embedding and the k-means algorithm. To this end we develop a representation based on the commute time between nodes on a graph. The commute time (i.e. the expected time taken for a random walk to travel between two nodes and return) can be computed from the Laplacian spectrum using the discrete Green’s function, and is an important property of the random walk on a graph. The commute time is a more robust measure of the proximity of data than the raw proximity matrix. Our embedding procedure preserves commute time, and is closely akin to kernel PCA, the Laplacian eigenmap and the diffusion map. We illustrate the results both on the synthetic image sequences and real world video sequences, and compare our results with several alternative methods.