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Poemapplication/pdfdepartmental bulletin pape

Improved Search for νμ→νe Oscillation in a Long-Baseline Accelerator Experiment

journal articl

尾小屋鉱山採鉱計画

東京帝国大学工科大学種別:卒業論文thesi

Supplementary Table 1 from TRAIL Signaling and Synergy Mechanisms Used in TRAIL-Based Combination Therapies

PDF file - 54K</p

平面図形の奥行視における、みえの大きさ、みえの距離、図形の場強の関係の実験的検討

1979-03The present study was concerned with the depth perception of bidimensional pattern. The author's attempt was to integrate bidimensional and tridimensional visual phenomena from a field-theoretical point of view. Experiment Ⅰ measured the distance perceived between observer and planes of the depth pattern by asking the subject to match the apparent distance of them with a frame pattern simultaneously presented to him. Experiment Ⅱ, dealing with the size-distance invariance, correlated the perceived size with the perceived distance of each plane in the depth pattern. The result confirmed the invariance. Experiment Ⅲ examined the relation between the field force and the perceived distance by the light-threshold method. The threshold of a small point projected on the plane varied with the perceived distance of the plane, showing a linear function of it. These findings were compared with previous reports by Motokawa et al.(1956), Hara(1970), and Nozawa(1971), mentioning possible factors producing the discrepancy and consistency in the results.departmental bulletin pape

The T-GAN-D stratifies TCGA patients despite these being scarcely represented in the merged training set.

(A) Schematic representing the training strategy: rescaled data from the entire MB cohort were merged with 4/5 of the TCGA cohort to train the T-GAN-D, which was subsequently used to predict the risk class of the remaining 1/5 of TCGA patients. The process was iterated 5 times. (B) Stratification of the TCGA patients by T-GAN-D trained on the merged dataset and (C) the MB dataset alone. Kaplan-Meier curves were generated pooling the predictions of all iterations of the 5-fold CV. The area between the curves (ABC) between Low risk (blue dashed line) and Predicted low risk (solid blue line), Predicted low risk and Predicted high risk (solid red line), Predicted high risk and High risk groups (dashed red line) are shown top to bottom in B and C.</p

Accuracy, sensitivity, specificity and Log-rank P value of each CV iteration and pooled category predictions for the experimental settings displayed in Figs 2 and S2.

Accuracy, sensitivity, specificity and Log-rank P value of each CV iteration and pooled category predictions for the experimental settings displayed in Figs 2 and S2.</p

S5 Fig -

Kaplan-Meier curves generated according to the risk class predicted by (A) RF and (B) SVM at each of the 5 CV iteration. Both models were trained with data from the merged MB + TCGA cohorts to predict the risk category of MB patients. At each iteration, a feature selection preprocessing step was performed on the training set. The following number of features was selected at each iteration: CViteration 1 = 32; CViteration 2 = 33; CViteration 3 = 33; CViteration 4 = 40; CViteration 5 = 40. (TIF)</p

The T-GAN-D outperforms classical biomarkers after merging the MB and TCGA cohorts and significantly stratifies early stage MB patients.

(A) Comparison of the hazard ratios (Cox model, univariate) of a multi-transcript signature (ROR-P) and established prognostic biomarkers (ER, HER2, PR) vs. the CNN and the T-GAN-D before and after cohort merging. (B) Multivariate Cox hazard ratio of the T-GAN-D compared to ROR-P and receptor status and (C) disease stage. (D) Kaplan -Meier curves of Stage I and (E) Stage II patients stratified by the T-GAN-D into low and high risk categories.</p

Accuracy, sensitivity, specificity and Log-rank P value of each CV iteration and pooled category predictions for the experimental settings displayed in Figs 3 and S3.

Accuracy, sensitivity, specificity and Log-rank P value of each CV iteration and pooled category predictions for the experimental settings displayed in Figs 3 and S3.</p