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  1. Isobe T, Takada H, Kanai M, Tsutsumi S, Isobe KO, Boonyatumanond R, et al.
    Environ Monit Assess, 2007 Dec;135(1-3):423-40.
    PMID: 17370135
    A comprehensive monitoring survey for polycyclic aromatic hydrocarbons (PAHs) and phenolic endocrine disrupting chemicals (EDCs) utilizing mussels as sentinel organisms was conducted in South and Southeast Asia as a part of the Asian Mussel Watch project. Green mussel (Perna viridis) samples collected from a total of 48 locations in India, Indonesia, Singapore, Malaysia, Thailand, Cambodia, Vietnam, and the Philippines during 1994-1999 were analyzed for PAHs, EDCs including nonylphenol (NP), octylphenol (OP) and bisphenol A (BPA), and linear alkylbenzenes (LABs) as molecular markers for sewage. Concentrations of NP ranged from 18 to 643 ng/g-dry tissue. The highest levels of NP in Malaysia, Singapore, the Philippines, and Indonesia were comparable to those observed in Tokyo Bay. Elevated concentrations of EDCs were not observed in Vietnam and Cambodia, probably due to the lower extent of industrialization in these regions. No consistent relationship between concentrations of phenolic EDCs and LABs were found, suggesting that sewage is not a major source of EDCs. Concentrations of PAHs ranged from 11 to 1,133 ng/g-dry, which were categorized as "low to moderate" levels of pollution. The ratio of methylphenanthrenes to phenanthrene (MP/P ratio) was >1.0 in 20 out of 25 locations, indicating extensive input of petrogenic PAHs. This study provides a bench-mark for data on the distribution of anthropogenic contaminants in this region, which is essential in evaluating temporal and spatial variation and effect of future regulatory measures.
  2. Sakaue S, Hirata J, Kanai M, Suzuki K, Akiyama M, Lai Too C, et al.
    Nat Commun, 2020 03 26;11(1):1569.
    PMID: 32218440 DOI: 10.1038/s41467-020-15194-z
    The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS.
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