The effects of quantitative easing on the USA, Japan, Eurozone and Great Britain

This master thesis aims to describe problematics of the effects of unconventional monetary policy, also known as quantitative easing, on economics of USA, Japan, Eurozone and Great Britain, by using empirical analysis of events related to quantitative easing and large BVAR model. In theoretical part of the thesis there are described transmission mechanisms of conventional monetary policy still effective in conditions of interest rates close to zero, as well as channels of unconventional monetary policy. Practical part of the thesis demonstrates analysis of impact of events related to quantitative easing in all the in-scope economics by applying a method of empirical observation of interest rates reactions on every event. Further, based on the received results of the empirical analysis, broader economic effects of quantitative easing are examined by using large BVAR model and afterwards the conclusion is made

The same trait-marker associations in Panel 2 and 3 using GLM model compared with those in Panel 1.

<p>Note: ^arepresents the locus identical with previous mapping results, <i>R²</i> represents the genetic variance explained by the marker.</p><p>The same trait-marker associations in Panel 2 and 3 using GLM model compared with those in Panel 1.</p

Association Mapping for Important Agronomic Traits in Core Collection of Rice (<i>Oryza sativa</i> L.) with SSR Markers

<div><p>Mining elite genes within rice landraces is of importance for the improvement of cultivated rice. An association mapping for 12 agronomic traits was carried out using a core collection of rice consisting of 150 landraces (Panel 1) with 274 simple sequence repeat (SSR) markers, and the mapping results were further verified using a Chinese national rice micro-core collection (Panel 2) and a collection from a global molecular breeding program (Panel 3). Our results showed that (1) 76 significant (<i>P</i><0.05) trait-marker associations were detected using mixed linear model (MLM) within Panel 1 in two years, among which 32% were identical with previously mapped QTLs, and 11 significant associations had >10% explained ratio of genetic variation; (2) A total of seven aforementioned trait-marker associations were verified within Panel 2 and 3 when using a general linear model (GLM) and 55 SSR markers of the 76 significant trait-marker associations. However, no significant trait-marker association was found to be identical within three panels when using the MLM model; (3) several desirable alleles of the loci which showed significant trait-marker associations were identified. The research provided important information for further mining these elite genes within rice landraces and using them for rice breeding.</p></div

Summary of association mapping results for 12 agronomic traits using MLM model in Panel 1.

<p>Note: In this table,</p>a<p>number of SSR loci shows the same trait-marker association (MLM, <i>P</i><0.05) in the both years;</p>b<p>number in parentheses represents the number of trait-marker associations which is located in the same or similar genomic region where QTLs were detected in previous studies;</p>c<p>the number of SSR loci showing the same trait-marker association (GLM, <i>P</i><0.05) in both years.</p><p>HD: Heading days, PH: Plant height, SS: Seed set rate, PL: Panicle length, GL: Grain length, GW: Grain width, 1000GW: 1000-grain weight, FLL: Flag leaf length, FLW: Flag leaf width and PN: Panicles number per plant.</p><p>Summary of association mapping results for 12 agronomic traits using MLM model in Panel 1.</p

Distribution of pairwise relative kinship values in Panel 1.

<p>The height of the black bar represents the percentage of varieties in different ranges of kinships.</p

Metabolic fate and subchronic biological effects of core–shell structured Fe₃O₄@SiO₂-NH₂ nanoparticles

<p>Core–shell structured Fe₃O₄@SiO₂-NH₂ nanoparticles (Fe@Si-NPs) demonstrated outstanding potentials in drug targeting and delivery and medical imaging. However, they have limited clinical applications due to unknown chronic bio-effects and potential bio-related risks. In this study, the subchronic biological effects and metabolic fate of 20 nm Fe@Si-NPs in Sprague–Dawley rats in 12 weeks were investigated by the biochemical assay and NMR-based metabonomic analysis using an intravenous model. Biofluids (plasma and urine) analysis provided the transportation, absorption, and excretion information of Fe@Si-NPs. Urine metabonome displayed a metabolic recovery while self-regulation of plasma metabonome leaded to the parallel metabolic trends between dosed and control groups in 12 weeks. And biological tissues (spleen, liver, kidney, and lung) analysis indicated liver and spleen are the targeted-organs of Fe@Si-NPs. The obvious metabolic variations responding to the biodistribution were induced by Fe@Si-NPs although no visible toxic effects were observed in these tissues. Besides the common energy metabolism response to the xenobiotics, Fe@Si-NPs also disturbed the metabolic pathways in glycerophospholipid and sphingolipid metabolism, metabolisms of purine, pyrimidine, and nicotinate. Our results provide preliminary validation for the potential use of Fe@Si-NPs in clinical medicine and give identifiable ground for the dose selection and bio-nanoagent optimization.</p

Duncan multiple comparisons for different allelic effects on traits.

<p>Note: Capital and lower letters represent significant difference at α = 0.01 and 0.05, respectively. Allele (bp) is PCR product amplified by SSR markers.</p><p>Duncan multiple comparisons for different allelic effects on traits.</p

Plots of observed versus expected <i>P</i>-values using MLM (Q+K) model for 12 agronomic traits in 2009.

<p>Blue symbol represents expected <i>P</i>-values, and red symbol represents observed <i>P</i>-values.</p

Plots of observed versus expected <i>P</i>-values using MLM (Q+K) model for 12 agronomic traits in 2008.

<p>The blue symbol the represents expected <i>P</i>-values, and the red symbol represents the observed <i>P</i>-values.</p

Association mapping results for 12 agronomic traits in two years using MLM model in Panel 1.

<p>Note:</p>a<p>BFmin with moderate to strong evidence for association (>0.05–0.13);</p>b<p>BFmin with strong to very strong evidence for association (≤0.05);</p>c<p>supported by the GLM in TASSEL (≤0.05);</p>d<p>the Bonferroni threshold (<0.0036);</p>e<p>supported by previous literature; <i>R²</i> represents the genetic variants explained by the marker; QTLs detected in previous studies (<a href="http://www.gramene.org/" target="_blank">http://www.gramene.org/</a>).</p><p>Association mapping results for 12 agronomic traits in two years using MLM model in Panel 1.</p