Displaying all 5 publications

  1. Zhong J, Jermusyk A, Wu L, Hoskins JW, Collins I, Mocci E, et al.
    J Natl Cancer Inst, 2020 10 01;112(10):1003-1012.
    PMID: 31917448 DOI: 10.1093/jnci/djz246
    BACKGROUND: Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown.

    METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).

    RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.

    CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

  2. Marshall N, Adger N, Attwood S, Brown K, Crissman C, Cvitanovic C, et al.
    PLoS One, 2017;12(3):e0171950.
    PMID: 28278238 DOI: 10.1371/journal.pone.0171950
    Failure to stem trends of ecological disruption and associated loss of ecosystem services worldwide is partly due to the inadequate integration of the human dimension into environmental decision-making. Decision-makers need knowledge of the human dimension of resource systems and of the social consequences of decision-making if environmental management is to be effective and adaptive. Social scientists have a central role to play, but little guidance exists to help them influence decision-making processes. We distil 348 years of cumulative experience shared by 31 environmental experts across three continents into advice for social scientists seeking to increase their influence in the environmental policy arena. Results focus on the importance of process, engagement, empathy and acumen and reveal the importance of understanding and actively participating in policy processes through co-producing knowledge and building trust. The insights gained during this research might empower a science-driven cultural change in science-policy relations for the routine integration of the human dimension in environmental decision making; ultimately for an improved outlook for earth's ecosystems and the billions of people that depend on them.
  3. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
  4. Nambiar P, Carson G, Taylor JA, Brown KA
    J Forensic Odontostomatol, 2001 Jun;19(1):5-8.
    PMID: 11494677
    A wad of used chewing gum recovered from the scene of a burglary contained impressions of human teeth. Casts of these impressions displayed unique morphological characteristics which were found to show concordance with corresponding features present on casts of the posterior teeth of a suspect.
  5. Zahidi I, Wilson G, Brown K, Hou FKK
    J Health Pollut, 2020 Dec;10(28):201207.
    PMID: 33324504 DOI: 10.5696/2156-9614-10.28.201207
    Background: Rivers are susceptible to pollution and water pollution is a growing problem in low- and middle-income countries (LMIC) with rapid development and minimal environmental protections. There are universal pollutant threshold values, but they are not directly linked to river activities such as sand mining and aquaculture. Water quality modelling can support assessments of river pollution and provide information on this important environmental issue.

    Objectives: The objective of the present study was to demonstrate water quality modelling methodology in reviewing existing policies for Malaysian river catchments based on an example case study.

    Methods: The MIKE 11 software developed by the Danish Hydraulic Institute was used to model the main pollutant point sources within the study area - sand mining and aquaculture. Water quality data were obtained for six river stations from 2000 to 2015. All sand mining and aquaculture locations and approximate production capacities were quantified by ground survey. Modelling of the sand washing effluents was undertaken with the advection-dispersion module due to the nature of the fine sediment. Modelling of the fates of aquaculture deposits required both advection-dispersion and Danish Hydraulic Institute ECO Lab modules to simulate the detailed interactions between water quality determinants.

    Results: According to the Malaysian standard, biochemical oxygen command (BOD) and ammonium (NH4) parameters fell under Class IV at most of the river reaches, while the dissolved oxygen (DO) parameter varied between Classes II to IV. Total suspended solids (TSS) fell within Classes IV to V along the mid river reaches of the catchment.

    Discussion: Comparison between corresponding constituents and locations showed that the water quality model reproduced the long-term duration exceedance for the main body of the curves. However, the water quality model underestimated the infrequent high concentration observations. A standard effluent disposal was proposed for the development of legislation and regulations by authorities in the district that could be replicated for other similar catchments.

    Conclusions: Modelling pollutants enables observation of trends over the years and the percentage of time a certain class is exceeded for each individual pollutant. The catchment did not meet Class II requirements and may not be able to reach Class I without extensive improvements in the quality and reducing the quantity of both point and non-point effluent sources within the catchment.

    Competing Interests: The authors declare no competing financial interests.

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