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Beach sand color is an important part of the coastal landscape. Some beaches are well known worldwide for their sand color, such as the black and green beaches in Hawaii. Sand color is also an important factor in beach ecology through beach thermal changes. White sandy beaches are usually made of coral and shell. Tan, brown and gray beaches are made of sand transported by rivers or the erosion of sandy cliffs. Although the color of a beach depends on the sediment supply budget, sand color has generally not been used for the analysis of beach processes and sedimentation. We performed color determination of sand grains collected from 164 sandy beaches with a scanner. The relative frequency of sand color rating was calculated using a color palette based on RGB color and the CIEL*a*b* 1976 color space methods. We determined the relationships between the color characteristics and the geological features of river drainage areas using principle component and cluster analysis. Sandy beaches in Japan were classified into two general groups based on color; gray and brown. The different colors represent the different types of minerals in the grains. The brightness of the sand grains can be explained by the areal ratios of the felsic and mafic rock. Artificial sandy beaches are consist of more colored grains because the color of the imported sediment is different from the native one and the sorting mechanism has not developed. So-called???singing sand??? beaches include many quarts and therefore contain tan and brown grains. The changes of the sand characteristics before and after the Tohoku tsunami in Japan were verified based on sand color information. Noticeable increases were also seen in the sand color brightness and the chromatic on the adjacent sandy beach of 1.4- and the chromatic 4-fold, respectively.

To establish the method for generating a large number of mature human mast cells, we cultured cord blood mononuclear cells (CBMC) in several conditions in the presence of Steel factor (SF). Among several cytokines tested, IL-6 enhanced SF-dependent mast cell growth from purified CD34+ cells for more than 8 wk in culture. When CBMC were cultured instead of CD34+ cells, IL-6 enhanced the mast cell development in the presence but not in the absence of PGE2. PGE2 enhanced the SF- and IL-6-dependent development of mast cells from CBMC probably by blocking granulocyte-macrophage CSF (GM-CSF) secretion from accessory cells, because 1) PGE2, or anti-GM-CSF enhanced the mast cell development induced by SF and IL-6 from CBMC, but not from CD34+ cells; 2) GM-CSF inhibited the enhancing effect of IL-6 on the mast cell development from CD34+ cells; and 3) PGE2 inhibited GM-CSF secretion from CBMC. The mast cells cultured in the presence of SF, IL-6, and PGE2 for >10 wk were 99% pure, and seemed to be functionally mature, because 1) they contained 5.62 micrograms of histamine and 3.46 micrograms of tryptase per 10(6) cells; and 2) when sensitized with human IgE and then challenged with anti-human IgE, the cells released a variety of mediators such as histamine, and an increase in intracellular Ca2+ was found in advance of the activation of membrane movement by using a confocal laser-scanning microscope. Electron-microscopic analysis revealed that some of the cultured mast cells are morphologically mature since they filled with scroll granules and contained crystal granules.

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As bio-medical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [ 48 ], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene co-occurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html