In a relative potency assessment, it is necessary to make assumptions about the similarities between substances and their dose-response profile. For example, in a parallel line bioassay which uses the dose-response data within the linear response range, we need to demonstrate that the dose-response slopes of the study substances are approximately parallel. When using multiple animals for testing, it is also crucial to confirm that this parallelism exists not only for the averages but also within each animal (Uehara et al. 2016a).

Meanwhile, when applying a linear mixed effect model to the analysis of parallel line assays, the between-substance difference of the slopes can be treated as a random effect. Thus, under a balanced assay design, we can derive an efficient score test to assess the quantile of the slope difference (McCulloch et al. 2008), which enables us to determine whether the majority of animals have their slope difference within the acceptable range.

We applied this approach to the assessment of intrasubject parallelism with the intention of ameliorating the conservatism of our previous method (Uehara et al. 2016b). We present an example that uses the proposed method, along with the results of simulation studies.

Bumblebees (Bombus spp.) are important pollinators of both wildflowers and crops. In recent year, declines of population size have been reported in many Bumblebee species in various countries. In order to design effective conservation plans, nest (colony) density in a habitat is an essentially important information. In the present study, a novel method for estimating nest density using molecular genetic markers was proposed, by extending the concept of neighborhood size in population genetics. The proposed method was compared with the conventional method used in previous studies by applying to microsatellite data of Bombus hypocrite sapporoensis. Based on the obtained estimates of nest density, advantage and disadvantage of the two methods were discussed. An interpretation of estimate obtained by the proposed method was also presented from a viewpoint of conservation genetics.

In recent years, analysis methods of microbiome data are developing rapidly, and many methods for the microbial compositional data which uses the 16S ribosomal RNA gene (16S rRNA data) are proposed. But, methods of association analysis for longitudinally measured 16S rRNA data are not studied well. Latent dirichlet allocation model (LDA) which is used mainly in natural language processing and has high expansion possibilities came to be applied to 16S rRNA data analysis in the past few years. Then, we propose an association analysis method by modifying existing LDA: topic tracking model for longitudinal 16S rRNA data. As the result of predictive performance evaluation, proposed method showed superior performance compared with topic tracking model with regard to perplexity. We applied this method to microbial data of rural Japanese people and identified topics associated with obesity.