This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed for the size of a real-world problem.. . .

The group of patients given 68 mg zinc showed a significant improvement in their gustatory sensitivity compared with the placebo group. The most common side effects observed were increase in serum triglyceride and serum alkaline phosphatase, decrease in serum iron, and some gastrointestinal incidents, although they were not serious.

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Purpose: We used serologic screening of a cDNA expression library of human testis to identify novel cancer/ testis antigens that elicit both humoral and cellular immune responses in cancer patients.Experimental Design and Results: We identified a novel gene designated KM-HN-1 the expression of which is testisspecific among normal tissues; it contains coiled coil domains and a leucine zipper motif and encodes a putative protein consisting of 833 amino acids. KM-HN-1 expression was observed in various cancer tissues and cancer cell lines at both mRNA and protein levels. Immunofluorescence staining of an esophageal cancer cell line revealed that KM-HN-1 protein was present exclusively in the nucleus during mitosis. Recombinant KM-HN-1 protein was produced, and used for ELISA to quantitate levels of IgG antibody specific to KM-HN-1. Higher levels of IgG antibodies specific to KM-HN-1 were detected in many types and numbers of cancer patients but not in healthy donors. The CTL lines specific to KM-HN-1, generated from HLA-A*2402-positive healthy donors and cancer patients, killed human leukocyte antigen (HLA)-A24-positive cancer cells expressing KM-HN-1 but not cell lines that did not express either KM-HN-1 or HLA-A24. Conclusions:We identified a novel cancer/testis antigen, KM-HN-1, which elicited humoral immune responses in patients with various types of cancer. Furthermore, KM-HN-1-specific CTLs could be generated from both healthy donors and cancer patients, which indicated that KM-HN-1 can be a candidate for an ideal target for cancer immunotherapy.

It is widely known that IL-4 and IL-13 act on various kinds of cells, including B cells, resulting in enhancement of proliferation, class switching to IgE and expression of several surface proteins. These functions are important for the recognition of the various antigens in B cells and are known to be involved in the pathogenesis of allergic diseases. However, it has not been known whether IL-4/IL-13 is involved in the metabolism of various kinds of xenobiotics including 2,3,7,8-tetra-chlorodibenzo-p-dioxin (TCDD), and it remains undetermined whether TCDD, an environmental pollutant, influences IgE production in B cells, exaggerating allergic reactions. We identified IL-4- or IL-13-inducible genes in a human Burkitt lymphoma cell line, DND-39, using microarray technology, in which the AHR gene was included. The AHR gene product, the aryl hydrocarbon receptor (AhR), was induced by IL-4 in both mouse and human B cells in a STAT6-dependent manner. IL-4 alone had the ability to translocate the induced AhR to the nuclei. TCDD, a ligand for AhR, rapidly degraded the induced AhR by the proteasomal pathway, although IL-4-activated AhR sustained its expression. AhR activated by IL-4 caused expression of a xenobiotic-metabolizing gene, CYP1A1, and TCDD synergistically acted on the induction of this gene by IL-4. However, the induction of AhR had no effect on IgE synthesis or CD23 expression. These results indicate that the metabolism of xenobiotics would be a novel biological function of IL-4 and IL-13 in B cells, whereas TCDD is not involved in IgE synthesis in B cells.

Prior exposure of dendritic cells (DCs) and monocytes/macrophages to LPS causes unresponsiveness to subsequent LPS stimulation, a phenomenon called endotoxin tolerance (ET). ET impairs antigen presentation of these cells to T cells by down-regulating expression of MHC class II and co-stimulatory molecules such as CD86 and CD40. Some epidemiological studies have shown that endotoxin acts as a protective factor for allergic diseases. Accordingly, LPS has beneficial effects on the onset of airway allergic inflammation in model animals by T(h)1 skewing or induction of regulatory T cells. However, results derived from asthma model animals are controversial, probably due to the difficulty of handling LPS. We previously generated a monoclonal agonistic antibody against Toll-like receptor (TLR) 4, named UT12, which mimics the biological activities of LPS, exhibiting more potent and sustained ET than does LPS. In this study, we took advantage of UT12 to generate prolonged ET to explore the possibility that ET is involved in the inhibitory effects of the TLR4 signals on asthma model mice. Induction of ET by UT12 inhibited the capacity of DCs to expand ovalbumin (OVA)-specific T(h)2 and T(h)17 cells, without inducing T(h)1 cell or regulatory T-cell populations or producing inhibitory cytokines. Accordingly, administration of UT12 before the OVA sensitization significantly suppressed airway allergic inflammation by OVA inhalation. Taken together, these results demonstrate that ET induced by activating TLR4 signals attenuates airway allergic inflammation through direct suppression of the T-cell stimulatory effect of DCs in asthma model mice.

The problem of mining frequent itemsets in transactional data has been studied frequently and has yielded several algorithms that can find the itemsets within a limited amount of time. Some of them can derive "generalized" frequent itemsets consisting of items at any level of a taxonomy [7]. Recently, several approaches have been proposed to mine frequent substructures (patterns) from a set of labeled graphs. The graph mining approaches are easily extended to mine generalized patterns where some vertices and/or edges have labels at any level of a taxonomy of the labels by extending the definition of "subgraph". However, the extended method outputs a massive set of the patterns most of which are over-generalized, which causes computation explosion. In this paper, an efficient and novel method is proposed to discover all frequent patterns which are not over-generalized from labeled graphs, when taxonomies on vertex and edge labels are available.