Representatiivsete proovide võtmine reostunud pinnase kuhjadest

Töö keskendub representatiivsete proovide võtmisele reostunud pinnase kuhjadest. Töö esimeses pooles antakse ülevaade proovivõtu teoreetilistest alustest. Käsitletakse pinnase partii mõõtmelisust ja heterogeensust ning kirjeldatakse proovivõtul tekkivaid võimalikke proovivõtu vigu. Samuti kirjeldatakse erinevaid viise, mida saab kasutada proovi massi vähendamiseks. Töö raames viidi läbi patendi teemauuring, et saada ülevaadet leiutistest, mida saaks rakendada proovide võtmisel saastunud pinnase kuhjadest. Uuringuga leiti viis sellekohast leiutist ning nende rakendatavust on töös võrreldud. Töö raames viidi läbi ka akrediteeringu-uuring, et tuvastada, millised Eestis tegutsevad asutused ja ettevõtted omavad akrediteeringut pinnaseproovide võtmiseks ja analüüsimiseks. Leiti kolm ettevõtet akrediteeringuga naftasaaduste sisalduse määramiseks pinnases ja neist ühel on ka akrediteering pinnaseproovide võtmiseks, kuid akrediteeritud meetod ei hõlma proovivõttu pinnasekuhjadest. Proovivõtu teoorias toodud praktilisi võtteid katsetati võrdleval proovivõtmisel saastunud pinnase töötlemise väljakul ja jääkreostuse likvideerimistöö objektil. Proove võeti kokku neljast pinnasekuhjast erinevatel meetoditel ning proovi massi vähendamiseks kasutati samuti erinevaid võtteid. Saadud proovides määrati akrediteeritud laborites naftasaaduste sisaldused ning saadud tulemusi on analüüsitud ning nende põhjal on antud hinnanguid kasutatud proovivõtu meetodite kohta. Töös on antud ka praktilised soovitused representatiivseks proovivõtuks pinnasekuhjadest. Töö eesmärk kirjeldada representatiivse proovivõtu metoodika reostunud pinnase kuhjadest proovide võtmiseks on täidetud. Kuna katsetulemused ei kinnitanud esimese auna puhul olulist erinevust representatiivse ja vähem representatiivse proovivõtu metoodika vahel, siis on antud töö baasil võimalik edasi uurida proovivõtu vigade ilmnemist suurema arvu proovivõtu puhul. Samuti on võimalik katseliselt kindlaks teha, kui kaua püsivad läbisegatud aunas 0D partii omadused ehk millise aja möödudes ei anna auna pinnalt proovide võtmine enam usaldusväärseid tulemusi

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

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.

<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.

<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.

<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.

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.

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

Additional file 1: Table S1: of Transcriptome analysis of Brassica napus pod using RNA-Seq and identification of lipid-related candidate genes

The top 20 most represented GO terms of DEG in the biological process category at the 5–7 DAF (S1-G1). (XLS 29 kb

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

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

Cohort-level genetic background analyses for Chinese cohorts under parsimony encG-reg analysis.

(A) Overview of the intersected SNPs across cohorts, a black dot indicated its corresponding cohort was included. Each row represented one cohort while each column represented one combination of cohorts. Dots linked by lines suggested cohorts in this combination. The height of bars represented the cohort’s SNP numbers (rows) or SNP intersection numbers (columns). Inset histogram plots show the distribution of the 7,009 intersected SNPs and the 500 SNPs randomly chosen from the 7,009 SNPs for encG-reg analysis. (B) 7,009 SNPs were used to estimate fPC from the intersection of SNPs for the 9 cohorts. Each triangle represented one Chinese cohort and was placed according to their first two principal component scores (fPC1 and fPC2) derived from the received allele frequencies. (C) Five private datasets have been pinned onto the base map from GADM (https://gadm.org/data.html) using R language. The size of point indicates the sample size of each dataset. (D) Global fStructure plot indicates global-level Fst-derived genetic composite projected onto the three external reference populations: 1KG-CHN (CHB and CHS), 1KG-EUR (CEU and TSI), and 1KG-AFR (YRI), respectively; 4,296 of the 7,009 SNPs intersected with the three reference populations were used. (E) Within Chinese fStructure plot indicates within-China genetic composite. The three external references are 1KG-CHB (North Chinese), 1KG-CHS (South Chinese), and 1KG-CDX (Southwest minority Chinese Dai), respectively; 4,809 of the 7,009 SNPs intersected with these three reference populations were used. Along x axis are 9 Chinese cohorts and the height of each bar represents its proportional genetic composition of the three reference populations. Cohort codes: YRI, Yoruba in Ibadan representing African samples; CHB, Han Chinese in Beijing; CHS, Southern Han Chinese; CHN, CHB and CHS together; CEU, Utah Residents with Northern and Western European Ancestry; TSI, Tuscani in Italy; CDX, Chinese Dai in Xishuangbanna.</p