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  1. Haga Y, Suzuki M, Matsumura C, Okuno T, Tsurukawa M, Fujimori K, et al.
    Environ Sci Pollut Res Int, 2018 Jun;25(17):16446-16454.
    PMID: 29656357 DOI: 10.1007/s11356-018-1927-0
    In this study, we analyzed hydroxylated polychlorinated biphenyls (OH-PCBs) in urine of both PCB transport workers and PCB researchers. A method to monitor OH-PCB in urine was developed. Urine was solid-phase extracted with 0.1% ammonia/ methanol (v/v) and glucuronic acid/sulfate conjugates and then decomposed using β-glucuronidase/arylsulfatase. After alkaline digestion/derivatization, the concentration of OH-PCBs was determined by HRGC/HRMS-SIM. In the first sampling campaign, the worker's OH-PCB levels increased several fold after the PCB waste transportation work, indicating exposure to PCBs. The concentration of OH-PCBs in PCB transport workers' urine (0.55~11 μg/g creatinine (Cre)) was higher than in PCB researchers' urine (
  2. Jim HS, Lin HY, Tyrer JP, Lawrenson K, Dennis J, Chornokur G, et al.
    J Genet Genome Res, 2015 09 15;2(2).
    PMID: 26807442
    Disruption in circadian gene expression, whether due to genetic variation or environmental factors (e.g., light at night, shiftwork), is associated with increased incidence of breast, prostate, gastrointestinal and hematologic cancers and gliomas. Circadian genes are highly expressed in the ovaries where they regulate ovulation; circadian disruption is associated with several ovarian cancer risk factors (e.g., endometriosis). However, no studies have examined variation in germline circadian genes as predictors of ovarian cancer risk and invasiveness. The goal of the current study was to examine single nucleotide polymorphisms (SNPs) in circadian genes BMAL1, CRY2, CSNK1E, NPAS2, PER3, REV1 and TIMELESS and downstream transcription factors KLF10 and SENP3 as predictors of risk of epithelial ovarian cancer (EOC) and histopathologic subtypes. The study included a test set of 3,761 EOC cases and 2,722 controls and a validation set of 44,308 samples including 18,174 (10,316 serous) cases and 26,134 controls from 43 studies participating in the Ovarian Cancer Association Consortium (OCAC). Analysis of genotype data from 36 genotyped SNPs and 4600 imputed SNPs indicated that the most significant association was rs117104877 in BMAL1 (OR = 0.79, 95% CI = 0.68-0.90, p = 5.59 × 10-4]. Functional analysis revealed a significant down regulation of BMAL1 expression following cMYC overexpression and increasing transformation in ovarian surface epithelial (OSE) cells as well as alternative splicing of BMAL1 exons in ovarian and granulosa cells. These results suggest that variation in circadian genes, and specifically BMAL1, may be associated with risk of ovarian cancer, likely through disruption of hormonal pathways.
  3. Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah YW, et al.
    Sci Data, 2020 07 09;7(1):225.
    PMID: 32647314 DOI: 10.1038/s41597-020-0534-3
    The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
  4. Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah YW, et al.
    Sci Data, 2021 Feb 25;8(1):72.
    PMID: 33633116 DOI: 10.1038/s41597-021-00851-9
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