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A total of 145 accessions of the genus Citrus and related genera, maintained in the Conservation Garden for Citrus Germplasm at the Experimental Farm of Kindai University, Yuasa, Wakayama, Japan, were examined for their phylogenetic relationships. The present classification was conducted using an inter-retrotransposon amplified polymorphism (IRAP) method based on the insertion polymorphism of a retrotransposon, CIRE1, identified in C. sinensis. The objective of this study was to evaluate the applicability of the IRAP method for citrus classification. The constructed dendrogram showed that the 145 accessions and two outgroup species were successfully classified into five major clades. A large number of C. sinensis accessions were divided into three traditional groups, navel orange, sweet orange, and blood orange, almost corresponding to the sub-clades in the dendrogram. Several other accessions belonging to the same species, and also many hybrid cultivars from crossbreeding, were localized into the respective sub-clades or near positions in the dendrogram. Several unclassified accessions could also be located in the dendrogram, suggesting novel relationships with other accessions. It was concluded that the IRAP method based on CIRE1 insertion polymorphism was suitable for the classification of citrus from a molecular point of view.

Fathers' involvement in childcare has been increasing in recent years. However, very little is known about the health impact on fathers when they are caring for or living with a disabled child. This study aims to understand the psychological distress and subjective health outcomes among fathers living with a disabled child compared to fathers living without a disabled child. Data for this study were obtained from the Comprehensive Survey of the Living Conditions conducted by the Ministry of Health, Labour and Welfare in 2016. Multivariable logistic regression was used among 438 disabled-child and father dyads and 27,682 non-disabled-child and father dyads to analyse the association between a child's disability status with father's health outcomes. Fathers of disabled children had a higher prevalence of psychological distress (17% vs. 12%) and poor subjective health status (13% vs. 8%) than fathers of non-disabled children. A large proportion of disabled children were boys (70%) and had disability level 1 (47%). After adjusting for covariates, the odds ratio (OR) of having psychological distress (OR, 1.53; 95% CI, 1.19–1.97) and poor subjective health status (OR, 1.78; 95% CI, 1.34–2.36) among fathers of disabled children is significantly higher compared to fathers of non-disabled children. Unemployed fathers had a higher odds ratio of psychological distress (OR, 3.07; 95% CI, 2.49–3.79) and poor subjective health status (OR, 4.90; 95% CI, 3.95–6.09) compared to regular working fathers. Fathers of children with disabilities need greater physical and mental health and wellbeing support. They should be provided with additional support not just for their mental but also their subjective wellbeing.

Abstract Background Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. Results This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. Conclusions This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.

Abstract Background The proportions of preterm birth (PTB, ie., delivered before 37 gestational weeks) and low birth weight (LBW, ie., birth weight less than 2500 g at delivery) have been rising in developed countries. We sought to examine the factors contributing to the rise in Japan, with particular focus on the effects of obstetric interventions. Methods We used a database maintained by one large regional hospital in Shizuoka, Japan. We restricted the analysis to mothers who delivered live singleton births from 1997 to 2010 (n = 19,221). We assessed the temporal trends in PTB and LBW, then divided the study period into four intervals and compared the proportions of PTB and LBW. We also compared the newborns’ outcomes between the intervals. Results PTB, in particular medically indicated PTB, increased considerably. The increase was largely explained by changes in caesarean sections. The neonatal outcomes did not worsen, and instead the Apgar scores and proportions requiring neonatal intensive care unit (NICU) admission improved. In particular, the risks of NICU admission in the interval from 2007 to 2010 were decreased among all births [odds ratio (OR): 0.84; 95% confidence interval (CI): 0.75, 0.95] and medically indicated births (OR: 0.44; 95% CI: 0.29, 0.68) compared with the interval from 1997 to 2000. Conclusions Despite the increases in PTB as well as LBW, the present study suggests benefits of obstetric interventions. Rather than simple categorization of PTB or LBW, indicators such as perinatal mortality or other outcomes may be more appropriate for evaluation of perinatal health in developed countries.

Abstract Background There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets. Results We introduce a new type of kernel called maximum entropy (ME) kernel, which has no pre-defined function but is generated by kernel entropy maximization with sample distance matrices as constraints, into the field of SVM classification of microarray data. We assessed the performance of the ME kernel with three different data: heterogeneous kidney carcinoma, noise-introduced leukemia, and heterogeneous oral cavity carcinoma metastasis data. The results clearly show that the ME kernel is very robust for heterogeneous data containing missing values and high-noise, and gives higher prediction accuracies than the standard kernels, namely, linear, polynomial and RBF. Conclusion The results demonstrate its utility in effectively analyzing promiscuous microarray data of rare specimens, e.g., minor diseases or species, that present difficulty in compiling homogeneous data in a single laboratory.