2,352 research outputs found
Super-Resolution and Self-Similarity in Magnetic Resonance Imaging
This thesis is about super-resolution reconstruction (SRR) and self-similarity in
MRI. These are two overlapping fields of research and in the studies described here,
one has naturally lead to the other. From investigating basic properties of conventional
approaches to SRR in MRI and applying these methods to specific research
problems, we saw a potential improvement to SRR in MRI by employing the selfsimilarity
of the images. Self-similarity is a versatile methodology, and beside using
it for SRR, we have performed a thorough investigation of its application to voxelwise
classification in MRI. In this introductory chapter, we will briefly give some
background on SRR and self-similarity in MRI and introduce the five studies included
in the thesis
Homoharringtonine, a clinically approved anti-leukemia drug, sensitizes tumor cells for TRAIL-induced necroptosis
Multiple sparse representations classification
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy.We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level
Allele-Specific Methylation Occurs at Genetic Variants Associated with Complex Disease
We hypothesize that the phenomenon of allele-specific methylation (ASM) may underlie the phenotypic effects of multiple variants identified by Genome-Wide Association studies (GWAS). We evaluate ASM in a human population and document its genome-wide patterns in an initial screen at up to 380,678 sites within the genome, or up to 5% of the total genomic CpGs. We show that while substantial inter-individual variation exists, 5% of assessed sites show evidence of ASM in at least six samples; the majority of these events (81%) are under genetic influence. Many of these cis-regulated ASM variants are also eQTLs in peripheral blood mononuclear cells and monocytes and/or in high linkage-disequilibrium with variants linked to complex disease. Finally, focusing on autoimmune phenotypes, we extend this initial screen to confirm the association of cis-regulated ASM with multiple complex disease-associated variants in an independent population using next-generation bisulfite sequencing. These four variants are implicated in complex phenotypes such as ulcerative colitis and AIDS progression disease (rs10491434), Celiac disease (rs2762051), Crohn's disease, IgA nephropathy and early-onset inflammatory bowel disease (rs713875) and height (rs6569648). Our results suggest cis-regulated ASM may provide a mechanistic link between the non-coding genetic changes and phenotypic variation observed in these diseases and further suggests a route to integrating DNA methylation status with GWAS results
A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk
Background: There is currently great interest in the incorporation of genetic susceptibility loci into screening models to identify individuals at high risk of disease. Here, we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA). Methods: A weighted genetic risk score (wGRS) was created using 45 RA non-human leucocyte antigen (HLA) susceptibility loci, imputed amino acids at HLA-DRB1 (11, 71 and 74), HLA-DPB1 (position 9) HLA-B (position 9) and gender. The wGRS was tested in 11 366 RA cases and 15 489 healthy controls. The risk of developing RA was estimated using logistic regression by dividing the wGRS into quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator characteristic analysis and discrimination improvement tests. Results: Individuals in the highest risk group showed significantly increased odds of developing anti-cyclic citrullinated peptide-positive RA compared to the lowest risk group (OR 27.13, 95% CI 23.70 to 31.05). The wGRS was validated in an independent cohort that showed similar results (area under the curve 0.78, OR 18.00, 95% CI 13.67 to 23.71). Comparison of the full wGRS with a wGRS in which HLA amino acids were replaced by a HLA tag single-nucleotide polymorphism showed a significant loss of sensitivity and specificity. Conclusions: Our study suggests that in RA, even when using all known genetic susceptibility variants, prediction performance remains modest; while this is insufficiently accurate for general population screening, it may prove of more use in targeted studies. Our study has also highlighted the importance of including HLA variation in risk prediction models
Single-shot velocity-map imaging of attosecond light-field control at kilohertz rate
High-speed, single-shot velocity-map imaging (VMI) is combined with carrier-
envelope phase (CEP) tagging by a single-shot stereographic above-threshold
ionization (ATI) phase-meter. The experimental setup provides a versatile tool
for angle-resolved studies of the attosecond control of electrons in atoms,
molecules, and nanostructures. Single-shot VMI at kHz repetition rate is
realized with a highly sensitive megapixel complementary metal-oxide
semiconductor camera omitting the need for additional image intensifiers. The
developed camerasoftware allows for efficient background suppression and the
storage of up to 1024 events for each image in real time. The approach is
demonstrated by measuring the CEP-dependence of the electron emission from ATI
of Xe in strong (≈1013 W/cm2) near single-cycle (4 fs) laser fields. Efficient
background signal suppression with the system is illustrated for the electron
emission from SiO2nanospheres
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Data for Genetic Analysis Workshop 16 Problem 1, Association Analysis of Rheumatoid Arthritis Data
For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid factor IgM. Several questions could be addressed using the data, including analysis of genetic associations using single SNPs or haplotypes, as well as gene-gene and genetic analysis of SNPs for qualitative and quantitative factors
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