メディア・ディスコースのイデオロギー表出ストラテジー : イラク戦争関連の社説における"We-group"の考察

application/pdf大阪府立大学言語文化研究. 2004, 3, p.9-19departmental bulletin pape

全学メールサービスの概要

2008-05departmental bulletin pape

Association between irregular daily routine and risk of incident stroke and coronary heart disease in a large Japanese population

Circadian misalignments have been linked to adverse cardiometabolic outcomes. However, the association between irregular daily routine and the risk of cardiovascular disease (CVD) remains unknown. We examined this association in a prospective study in Japan. The study included 78,115 Japanese participants aged 45–74 years. The self-reported daily routine was evaluated using the question, ‘Is your daily routine or activity schedule regular?’ The response (yes/no) was obtained as a binary variable. Cox proportional hazard regression analysis was used to estimate the hazard ratios and 95% confidence intervals for the association between an irregular daily routine and CVD incidence risk. Among the participants, 23.7% reported an irregular daily routine. During the mean follow-up period of 13.3 years, we observed 4641 CVD events. An irregular daily routine was significantly associated with increased risks of CVD and total stroke in women, but not in men. This positive association between an irregular daily routine and the risk of CVD was weak in the high vegetable and fruit consuming population. An irregular daily routine is positively associated with the risk of incident CVD, especially in women. These associations may be weak in populations that consume a diet rich in vegetables and fruits.journal articl

Improved Constraints on D0-D̅0 Mixing in D0→K+π- Decays from the Belle Detector

journal articl

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Top 20 DDI predictions in the test set.

Top 20 DDI predictions in the test set.</p

The train-test splitting scheme and model performance on the test set.

(a) The train-test splitting scheme. Drugs are randomly divided into “training drugs” and “test drugs” with ratio of 2:1. Training set only consists of drug pairs constituted by “training drugs” and test set only consists of drug pairs constituted by “test drugs”. Training drugs are further split into “training drugsi” and “validation drugsi” with the same splitting scheme to obtain training seti and validation seti in the training phase. For each iteration of hold-out validation, the classifier is fit with training seti and evaluated with validation seti. Purple squares represent non-interacting drug pairs in training seti. Blue squares represent non-interacting drug pairs in validation seti. Green squares represent non-interacting drug pairs in test set. Red squares represent interacting drug pairs in each set. Grey squares represent unused drug pairs. (b) Approximate receiver operating characteristic (ROC) curves on the training set. (c) Approximate precision-recall curves on the training set. (d) AUROCs and AUPRs on the training set and the test set. (e) Receiver operating characteristic (ROC) curve on the test set. (f) Precision-recall curve on the test set.</p

Top 20 new adverse DDI predictions.

Top 20 new adverse DDI predictions.</p

The number of side effects is positively correlated with target centrality in the protein-protein interaction network.

<p>(<b>A</b>) Network representation of the human protein-protein interactome for drug targets. Nodes represent proteins and edges correspond to interactions. Colored nodes in the panel (A) indicate the known drug targets. The number of drug side effects is positively correlated with (<b>B</b>) the degree of a target, (<b>C</b>) the number of bottleneck targets, and (<b>D</b>) the proportion of shared interaction interface on a target. All the results are obtained from generalized linear regressions based on negative binomial distribution for side effects. Gray symbols in the panels (B)–(D) are raw data while the colored ones correspond to median counts of side effects. Schematics under the x-axes illustrate a drug (hexagon) binding to its target protein(s) (filled circles): In (B), open circles represent interaction partners of the drug targets. In (C), the filled circle is a bottleneck target and open circles represent non-bottleneck proteins in the network. In (D), different interfaces of a multi-interface drug target are highlighted in colors; the interface of a single-interface drug target is highlighted in black.</p

Adversely interacting drug pairs and non-interacting drug pairs significantly differ with regard to the 11 features selected.

(a) Schematics of calculating indication similarity and side effect similarity features. (b) Indication similarity score of hierarchy level PT, HLGT and SOC between two drugs. (c) Side effect similarity score of hierarchy level HLT and HLGT between two drugs. (d) Schematics of calculating target sequence similarity and genetic interaction features. Genetic interaction scores indicate the deviation from the expected phenotype when two genes are simultaneously knocked out, and were obtained from a global genetic interaction network in yeast by mapping targets of drugs to their yeast homologs. A negative score denotes synergistic interaction while a positive score indicates buffering interaction. (e) Minimum, mean, median and maximum target sequence similarity score between targets of two drugs. (f) Minimum and maximum genetic interaction score between targets of two drugs. Statistical significance was determined by the two-sided permutation test on the sample mean. PT, preferred term; HLT, high level term; HLGT, high level group term; SOC, system organ class. * p < 0.001; ** p < 0.0001; *** p < 0.00001.</p