33 research outputs found

    Representatiivsete proovide vÔtmine reostunud pinnase kuhjadest

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    Töö keskendub representatiivsete proovide vĂ”tmisele reostunud pinnase kuhjadest. Töö esimeses pooles antakse ĂŒlevaade proovivĂ”tu teoreetilistest alustest. KĂ€sitletakse pinnase partii mÔÔtmelisust ja heterogeensust ning kirjeldatakse proovivĂ”tul tekkivaid vĂ”imalikke proovivĂ”tu vigu. Samuti kirjeldatakse erinevaid viise, mida saab kasutada proovi massi vĂ€hendamiseks. Töö raames viidi lĂ€bi patendi teemauuring, et saada ĂŒlevaadet leiutistest, mida saaks rakendada proovide vĂ”tmisel saastunud pinnase kuhjadest. Uuringuga leiti viis sellekohast leiutist ning nende rakendatavust on töös vĂ”rreldud. Töö raames viidi lĂ€bi ka akrediteeringu-uuring, et tuvastada, millised Eestis tegutsevad asutused ja ettevĂ”tted omavad akrediteeringut pinnaseproovide vĂ”tmiseks ja analĂŒĂŒsimiseks. Leiti kolm ettevĂ”tet akrediteeringuga naftasaaduste sisalduse mÀÀramiseks pinnases ja neist ĂŒhel on ka akrediteering pinnaseproovide vĂ”tmiseks, kuid akrediteeritud meetod ei hĂ”lma proovivĂ”ttu pinnasekuhjadest. ProovivĂ”tu teoorias toodud praktilisi vĂ”tteid katsetati vĂ”rdleval proovivĂ”tmisel saastunud pinnase töötlemise vĂ€ljakul ja jÀÀkreostuse likvideerimistöö objektil. Proove vĂ”eti kokku neljast pinnasekuhjast erinevatel meetoditel ning proovi massi vĂ€hendamiseks kasutati samuti erinevaid vĂ”tteid. Saadud proovides mÀÀrati akrediteeritud laborites naftasaaduste sisaldused ning saadud tulemusi on analĂŒĂŒsitud ning nende pĂ”hjal on antud hinnanguid kasutatud proovivĂ”tu meetodite kohta. Töös on antud ka praktilised soovitused representatiivseks proovivĂ”tuks pinnasekuhjadest. Töö eesmĂ€rk kirjeldada representatiivse proovivĂ”tu metoodika reostunud pinnase kuhjadest proovide vĂ”tmiseks on tĂ€idetud. Kuna katsetulemused ei kinnitanud esimese auna puhul olulist erinevust representatiivse ja vĂ€hem representatiivse proovivĂ”tu metoodika vahel, siis on antud töö baasil vĂ”imalik edasi uurida proovivĂ”tu vigade ilmnemist suurema arvu proovivĂ”tu puhul. Samuti on vĂ”imalik katseliselt kindlaks teha, kui kaua pĂŒsivad lĂ€bisegatud aunas 0D partii omadused ehk millise aja möödudes ei anna auna pinnalt proovide vĂ”tmine enam usaldusvÀÀrseid tulemusi

    Information on the SNPs in the best model identified using GEE-GMDR method.

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    a<p>The nucleotide of each SNP shown in bold represents the minor allele as given in dbSNP (build 138).</p>b<p>The minor allele frequency (MAF) presented in dbSNP (build 138).</p><p>Information on the SNPs in the best model identified using GEE-GMDR method.</p

    Quantile-quantile plot of significance level and Type I error rate.

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    <p>The Type I error is evaluated by GEE-GMDR method with digenic, trigenic and tetragenic models in presence of no gene-gene interaction and no residual correlation. The reference line is a diagonal line with unit slope through the origin. An unbiased method is expected to give the points falling on or near the reference line (i.e., Type I error rate is very close to the nominal level).</p

    Comparison of statistical power between univarate GMDR method and GEE-GMDR under digenic, trigenic and tetragenic interaction models.

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    <p>The horizontal axis represents different residual correlations. The empirical statistical power is defined as the proportion of significant true models at 5% level in 200 simulations.</p

    The interaction pattern among rs2072660-rs1209068-rs11030134-rs6011770.

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    <p>The left bar in each nonempty cell denotes a positive score and the right bar a negative score. High-risk cells are indicated by dark shading, low-risk cells by light shading, and empty cells by no shading. Note that the patterns of high-risk and low-risk cells differ across each of the different multilocus dimensions, presenting evidence of epistasis.</p

    Type I error rates for GEE-GMDR and GMDR methods.

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    a<p>GEE-GMDR is the GEE-GMDR analysis for the simulated bivariate traits, GMDR-T1 is the univariate GMDR analysis for trait 1, and GMDR-T2 is the univariate GMDR analysis for trait 2.</p><p>Type I error rates for GEE-GMDR and GMDR methods.</p

    The principal components analysis for SAGE.

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    <p>The first two principal components are plotted to represent genetic background of the SAGE.</p

    Resolution for varying relatedness using GRM, encGRM and <i>encG-reg</i>.

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    The figure shows the resolution for detecting relatives or overlapping samples with respect to varying number of markers at every row (for better illustration me was twice that of Eq 3) and the degree of relatives to be detected (r = 0, 1, and 2). The y axis is the relatedness calculated from GRM and the x axis is the estimated relatedness calculated from encG-reg (A) and encGRM (B). Each point represents an individual pair between cohort 1 and cohort 2 (there are 200 × 200 = 40,000 pairs in total), given the simulated relatedness. The dotted line indicates the 95% confidence interval of the relatedness directly estimated from the original genotype (blue) and the encrypted genotype (red). The table provides how m and k are estimated. The columns “under minimal me” provide benchmark for a parameter, and it is practically to choose 2×me and then estimate k as shown under the column “practical me”.</p

    Workflow of <i>encG-reg</i> and its practical timeline as exercised in Chinese cohorts.

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    The mathematical details of encG-reg are simply algebraic, but its inter-cohort implementation involves coordination. (A) We illustrate its key steps, the time cost of which was adapted from the present exercise for 9 Chinese datasets (here simplified as three cohorts). Cohort assembly: It took us about a week to call and got positive responses from our collaborators (See Table 3), who agreed with our research plan. Inter-cohort QC: we received allele frequencies reports from each cohort and started to implement inter-cohort QC according to “geo-geno” analysis (see Fig 6). This step took about two weeks. Encrypt genotypes: upon the choice of the exercise, it could be exhaustive design (see UKB example), which may maximize the statistical power but with increased logistics such as generating pairwise Sij; in the Chinese cohorts study we used parsimony design, and generated a unique S given 500 SNPs that were chosen from the 7,009 common SNPs. It took about a week to determine the number of SNPs and the dimension of k according to Eq 3 and 4, and to evaluate the effective number of markers. Perform encG-reg and validation: we conducted inter-cohort encG-reg and validated the results (see Fig 7 and Table 4). It took one week. (B) Two interactions between data owners and central analyst, including example data for exchange and possible attacks and corresponding preventative strategies.</p