Proton and neutron correlations in 10^{10}B

We investigate positive-parity states of $^{10}$B with the calculation of antisymmetrized molecular dynamics focusing on $pn$ pair correlations. We discuss effects of the spin-orbit interaction on energy spectra and $pn$ correlations of the $J^\pi T=1^+_10$, $=3^+_10$, and $0^+_11$ states. The $1^+_10$ state has almost no energy gain of the spin-orbit interaction, whereas the $3^+_10$ state gains the spin-orbit interaction energy largely to come down to the ground state. We interpret a part of the two-body spin-orbit interaction in the adopted effective interactions as a contribution of the genuine $NNN$ force, and find it to be essential for the level ordering of the $3^+_10$ and $1^+_10$ states in $^{10}$B. We also apply a $2\alpha+pn$ model to discuss effects of the spin-orbit interaction on $T=0$ and $T=1$ $pn$ pairs around the 2$\alpha$ core. In the spin-aligned $J^\pi T=3^+0$ state, the spin-orbit interaction affects the $(ST)=(10)$ pair attractively and keeps the pair close to the core, whereas, in the $1^+0$ state, it gives a minor effect to the $(ST)=(10)$ pair. In the $0^+1$ state, the $(ST)=(01)$ pair is somewhat dissociated by the spin-orbit interaction.Comment: 12 pages 9 figure

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning

Adverse events are a serious issue in drug development and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach tends to be overoptimistic compared with the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not fully understood. To understand the differences, we compared the model performance between the time and random splits using eight types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events.Comment: 20 pages, 4 figure

Isolation and Structure Characterization of Flavonoids

Flavonoids are one of the most important classes of secondary metabolites from natural products due to their several applications in medicine, foods, diet industries, and so on. Even though a huge number has been reported from natural and synthetic sources, scientists are still interested in flavonoids and derivatives. The biggest challenge for working on secondary metabolites is related to the use of the predicted theoretical method to isolate the expected compound and finally analyse the spectroscopic data to elucidate and fully characterize the structure. This chapter was designed to document useful techniques for isolation and structure characterization of flavonoids. Besides the well-known methods that have been used so far, we would also put together updated information about novel challenge techniques published in recent articles on isolation and characterization of flavonoids. Our data were obtained mainly from academic library and from reported data online by using research links such as Google Scholar, Scopus, SciFinder, Scirus, PubMed, and so on. Our field experience on phytochemistry of isolation and characterization of flavonoids was also used in this chapter

Genome-Wide Association Study of Coronary Artery Disease

Coronary artery disease (CAD) is a multifactorial disease with environmental and genetic determinants. The genetic determinants of CAD have previously been explored by the candidate gene approach. Recently, the data from the International HapMap Project and the development of dense genotyping chips have enabled us to perform genome-wide association studies (GWAS) on a large number of subjects without bias towards any particular candidate genes. In 2007, three chip-based GWAS simultaneously revealed the significant association between common variants on chromosome 9p21 and CAD. This association was replicated among other ethnic groups and also in a meta-analysis. Further investigations have detected several other candidate loci associated with CAD. The chip-based GWAS approach has identified novel and unbiased genetic determinants of CAD and these insights provide the important direction to better understand the pathogenesis of CAD and to develop new and improved preventive measures and treatments for CAD