Technological Advances in Plant Innovation and the Dynamics of Intellectual Property Law

名古屋大学Nagoya University博士（法学）doctoral thesi

Cytogenetical Behaviour of the B Chromosomes of Rye in Hexaploid Wheat

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Suppression of skeletal muscle atrophy by lysine and lysine-related compounds

岩手大学博士（農学）doctoral thesi

Development of a permanent magnet type gyrotron using higher harmonics

A novel high harmonic gyrotron with an axis-encircling beam is under development in collaboration between the Research Center for Development of Far-Infrared Region at Fukui University and Shin-Etsu Chemical. In this paper we present results from a computer aided design of the permanent magnet system for such prospective gyro-device. The results of numerical simulations predict that it will produce an appropriate field distribution with maximum value of 1 T at the cavity region.research repor

Identification of the prognostic reproducibility of markers.

<p>Their prognostic ability was examined in an independent test data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107925#pone.0107925-Sheffer1" target="_blank">[22]</a> by supervised classifications and thus confirmed their prognostic reproducibility. (<b>A</b>) Prognostic ability of our bridge proteins (<b>B</b>) Prognostic ability determined by the ColoPrint gene set in reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107925#pone.0107925-Salazar1" target="_blank">[28]</a> (<b>C</b>) Prognostic ability determined by the Wang et al. gene set in reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107925#pone.0107925-Wang1" target="_blank">[27]</a>.</p

Identification of the prognostic ability of markers.

<p>Their prognostic ability was examined using a dataset of tissue samples from patients with CRC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107925#pone.0107925-Marisa1" target="_blank">[26]</a>. (<b>A</b>) Heatmap of CRC tumor samples with subgroups classified by the expression patterns of bridge proteins: BA-m1 (blue), BA-m2 (yellow) and BA-m3 (red). Prognostic ability was assessed by Kaplan-Meier survival analyses. The BA-m2 group showed the poorest prognosis. (<b>B</b>) Prognostic ability of our bridge proteins. (<b>C</b>) Prognostic ability of the ColoPrint gene set <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107925#pone.0107925-Salazar1" target="_blank">[28]</a>, with subgroups classified as col-m1 (blue), col-m2 (yellow) and col-m3 (red). (<b>D</b>) Prognostic ability of the Wang et al. signature gene set <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107925#pone.0107925-Wang1" target="_blank">[27]</a>, with subgroups classified as wang-m1 (blue), wang-m2 (yellow) and wang-m3 (red). (<b>E</b>) Prognostic ability of p53 mutation status, mutant and wild-type. (<b>F</b>) Prognostic ability of mismatch repair gene (MMR) status, deficient (dMMR) and proficient (pMMR). (<b>G</b>) Prognostic ability of KRAS mutation status, mutant and wild-type. (<b>H</b>) Prognostic ability of BRAF mutation status, mutant and wild-type.</p

Anomalies in Network Bridges Involved in Bile Acid Metabolism Predict Outcomes of Colorectal Cancer Patients

<div><p>Biomarkers prognostic for colorectal cancer (CRC) would be highly desirable in clinical practice. Proteins that regulate bile acid (BA) homeostasis, by linking metabolic sensors and metabolic enzymes, also called bridge proteins, may be reliable prognostic biomarkers for CRC. Based on a devised metric, “bridgeness,” we identified bridge proteins involved in the regulation of BA homeostasis and identified their prognostic potentials. The expression patterns of these bridge proteins could distinguish between normal and diseased tissues, suggesting that these proteins are associated with CRC pathogenesis. Using a supervised classification system, we found that these bridge proteins were reproducibly prognostic, with high prognostic ability compared to other known markers.</p></div

Multivariate analysis of bridge proteins from different networks.

<p>(<b>A</b>) Overall process of multivariate classifications using features from bridge proteins of different networks. After sorting bridge proteins by their bridgeness (), features were extracted cumulatively from top-ranked bridge proteins (). Samples were subsequently classified by cumulatively selected features and calculated classification accuracies (). (<b>B</b>) Accuracies of classifications between normal colon and primary CRC tissues. For classifications, bridge proteins were obtained from (i) a bile acid bridge network (red), (ii) a glycolysis bridge network (yellow) and (iii) an whole protein network (purple). Classification accuracies were also calculated using randomly selected proteins (black) with 95% confidence intervals (gray) on the mean classification accuracies of repeated random selections (<b>C</b>) Accuracies of classifications between normal colon and polyp tissues. (<b>D</b>) Accuracies of classifications between polyp and primary CRC tissues.</p

7 Sheehan症候群の1例(I.一般演題,第86回新潟内分泌代謝同好会)

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