Causal relations of health indices inferred statistically using the DirectLiNGAM

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012–2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than 104. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes

Mao-to Prolongs the Survival of and Reduces TNF-α Expression in Mice with Viral Myocarditis

Goal of this study was to evaluate effects of Mao-to on development of myocarditis induced by encephalomyocarditis (EMC) virus in mice. Mice were randomly divided into five groups. Group N included uninfected controls (n = 18), while group A, B and C underwent intraperitoneal injection of EMC virus. Group A was administered oral saline from day 0 to day 4. Group B was administered oral Mao-to (500 mg−1 kg−1 day−1) from day 0 to day 4. Group C was administered Mao-to from day 2 to day 6. Group D was administered Mao-to from day 5 to day 10. Treated mice were followed for survival rates during 2 weeks after infection. Body weight (BW) and organ weights including heart (HW), lungs, thymus and spleen were examined on days 4, 6 and 14. Survival rate of group C (36.4%) was significantly improved compared with group A, B or D (0% of each, P < 0.05). HW and HW/BW ratio in group C was significantly (P < 0.05) lower than those in group A, B or D. Viral titers of hearts were significantly different among groups A, B and C. Cardiac expression in tumor necrosis factor-α (TNF-α) was significantly reduced in group C in comparison with group A, B or D on day 6 by immunohistochemical study. Administration of Mao-to starting on day 2 improves mortality resulting from viral myocarditis in mice with reduced expression of cardiac TNF-α. These findings suggest that timing of Mao-to is crucial for preventing cardiac damage in mice with viral myocarditis

The status of DECIGO

DECIGO (DECi-hertz Interferometer Gravitational wave Observatory) is the planned Japanese space gravitational wave antenna, aiming to detect gravitational waves from astrophysically and cosmologically significant sources mainly between 0.1 Hz and 10 Hz and thus to open a new window for gravitational wave astronomy and for the universe. DECIGO will consists of three drag-free spacecraft arranged in an equilateral triangle with 1000 km arm lengths whose relative displacements are measured by a differential Fabry-Perot interferometer, and four units of triangular Fabry-Perot interferometers are arranged on heliocentric orbit around the sun. DECIGO is vary ambitious mission, we plan to launch DECIGO in era of 2030s after precursor satellite mission, B-DECIGO. B-DECIGO is essentially smaller version of DECIGO: B-DECIGO consists of three spacecraft arranged in an triangle with 100 km arm lengths orbiting 2000 km above the surface of the earth. It is hoped that the launch date will be late 2020s for the present