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Fan Chung

Publications and source records attributed to Fan Chung.

7 recordsLinked to original sources
PubMed2005

Protease-activated receptor 2 expression in trigeminal neurons innervating the rat nasal mucosa.

Protease-activated receptor 2 (PAR2) is activated by trypsin and mast cell tryptase to induce widespread inflammation by unknown mechanisms. Trypsin and tryptase were shown to activate sensory neurons to release substance-P and related peptides to mediate neurogenic inflammation. In the present study, the expression of PAR2 and tachykinins were investigated in rat trigeminal neurons that were identified by retrograde labeling with rhodamine dye from the nasal mucosa by using neuronal tracing in combination with immunohistochemistry. We found that large subpopulation of all trigeminal neurons (43.5+/-2.6%) identified by the pan-neuronal marker PGP 9.5 were stained with PAR2-immunoreactivity. Of all trigeminal neurons, 7.5+/-2.1% were immunoreactive for tachykinins and PAR2, and only 3.9+/-1.7% of all trigeminal neurons expressed tachykinins, but not PAR2-immunoreactivity. The present study also found that a large number trigeminal neurons innervating the nasal mucosa expressed PAR2-immunoreactivity. Of the rhodamine-labeled trigeminal neurons, 52.5+/-1.8% were immunoreactive for only PAR2 expression, 7.3+/-1.9% contained tachykinins and PAR2, and 3.1+/-0.4 of the rhodamine-labeled trigeminal neurons were non-immunoreactive PAR2, but were positive for tachykinins-immunoreactivity. In conclusion, based on the co-localization of PAR2 and tachykinins in trigeminal sensory neurons innervating the nasal mucosa, the present study suggests that, following an activation of PAR2 receptor in tachykinergic neurons by trypsin and mast cell tryptase, there may be a triggering of tachykinin-mediated phenomena such as neurogenic inflammation in allergic or non-allergic rhinitis.

Animals↗
PubMed2005

Identification and expression of a new type II transmembrane protein in human mast cells.

A cDNA encoding a new type II transmembrane protein has been isolated from human mast cells by subtraction cloning. This cDNA contains an open reading frame of 186 amino acids. RT-PCR analysis showed that this gene is differentially expressed in mast cells. Therefore, the peptide encoded by this gene was termed mast cell-expressed membrane protein 1 (MCEMP1). The MCEMP1 gene contains seven exons and was mapped to human chromosome 19p13.3. The epitope-tagged MCEMP1 has been expressed in mammalian cells and found to be localized to the cellular membrane with its C-terminus extending to the outside of the membrane and N-terminus into the cytoplasmic compartment. Monoclonal antibodies against MCEMP1 were generated and characterized by immunoprecipitation and FACS. The results showed that the native MCEMP1 is expressed in cord blood-derived mast cells and HMC-1 and THP-1 cell lines, but not in other cell types that we have tested. Immunochemical staining of human lung sections showed that MCEMP1 staining is specifically associated with lung mast cells.

Alternative Splicing↗
PubMed2004

Spectral grouping using the Nyström method.

Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation. However, due to the computational demands of these approaches, applications to large problems such as spatiotemporal data and high resolution imagery have been slow to appear. The contribution of this paper is a method that substantially reduces the computational requirements of grouping algorithms based on spectral partitioning making it feasible to apply them to very large grouping problems. Our approach is based on a technique for the numerical solution of eigenfunction problems known as the Nyström method. This method allows one to extrapolate the complete grouping solution using only a small number of samples. In doing so, we leverage the fact that there are far fewer coherent groups in a scene than pixels.

Algorithms↗
PubMed2003

Spectra of random graphs with given expected degrees.

In the study of the spectra of power-law graphs, there are basically two competing approaches. One is to prove analogues of Wigner's semicircle law, whereas the other predicts that the eigenvalues follow a power-law distribution. Although the semicircle law and the power law have nothing in common, we will show that both approaches are essentially correct if one considers the appropriate matrices. We will prove that (under certain mild conditions) the eigenvalues of the (normalized) Laplacian of a random power-law graph follow the semicircle law, whereas the spectrum of the adjacency matrix of a power-law graph obeys the power law. Our results are based on the analysis of random graphs with given expected degrees and their relations to several key invariants. Of interest are a number of (new) values for the exponent beta, where phase transitions for eigenvalue distributions occur. The spectrum distributions have direct implications to numerous graph algorithms such as, for example, randomized algorithms that involve rapidly mixing Markov chains.

Journal Article↗
PubMed2003

Duplication models for biological networks.

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N(k) approximately k(-beta)), yet the exponents, beta, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

Internet↗
PubMed2002

The average distances in random graphs with given expected degrees.

Random graph theory is used to examine the "small-world phenomenon"; any two strangers are connected through a short chain of mutual acquaintances. We will show that for certain families of random graphs with given expected degrees the average distance is almost surely of order log nlog d, where d is the weighted average of the sum of squares of the expected degrees. Of particular interest are power law random graphs in which the number of vertices of degree k is proportional to 1kbeta for some fixed exponent beta. For the case of beta > 3, we prove that the average distance of the power law graphs is almost surely of order log nlog d. However, many Internet, social, and citation networks are power law graphs with exponents in the range 2 < beta < 3 for which the power law random graphs have average distance almost surely of order log log n, but have diameter of order log n (provided having some mild constraints for the average distance and maximum degree). In particular, these graphs contain a dense subgraph, which we call the core, having n(clog log n) vertices. Almost all vertices are within distance log log n of the core although there are vertices at distance log n from the core.

Journal Article↗