| 1. |
- Feroci, M., et al.
(författare)
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The large observatory for x-ray timing
- 2014
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 9780819496126
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Konferensbidrag (refereegranskat)abstract
- The Large Observatory For x-ray Timing (LOFT) was studied within ESA M3 Cosmic Vision framework and participated in the final downselection for a launch slot in 2022-2024. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument, LOFT will study the behaviour of matter under extreme conditions, such as the strong gravitational field in the innermost regions of accretion flows close to black holes and neutron stars, and the supranuclear densities in the interior of neutron stars. The science payload is based on a Large Area Detector (LAD, 10 m2 effective area, 2-30 keV, 240 eV spectral resolution, 1° collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e.g. GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the status of the mission at the end of its Phase A study.
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| 2. |
- Nettelblad, Carl, et al.
(författare)
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Assessing orthogonality and statistical properties of linear regression methods for interval mapping with partial information
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Background: Mapping quantitative trait loci (QTL) has become a widely used tool in genetical research. In such experiments, it is desired to obtain orthogonal estimates of genetic effects for a number of reasons concerning both the biological meaning of the estimated locations and effects, and making the statistical analysis clearer and more robust. The currently used statistical methods, however, are not optimized for orthogonality, especially in cases involving interval mapping between markers and/or in incomplete datasets. This is an adverse limitation for the application of such methods for QTL scans involving model selection over putative complex gene networks.Results: We describe how deviations from orthogonality arise in currently used methods. We demonstrate one option for obtaining orthogonal estimates of genetic effects using multiple imputations per individual in an otherwise unchanged regression context. Our proposed IRIM method avoids inflated values for explainable variance and genetic effect variables, while showing a clear preference for marker locations in a fine mapping context. Despite possible shortcomings, similar results to linear regression are demonstrated for our proposed approach (IRIM) in an experimental dataset.Conclusions: Imputation-based methods can be used to enhance the statistical dissectability of effects, as well as computational performance. We exemplify how Haley-Knott regression is not only distorting the explainable variance, but also point out how the estimated phenotype values between classes, and the resulting effects, become dependent. This illustrates the need for a more radical departure in the approach chosen in order to achieve orthogonality.
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| 3. |
- Nettelblad, Carl, 1985-
(författare)
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Two Optimization Problems in Genetics : Multi-dimensional QTL Analysis and Haplotype Inference
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The existence of new technologies, implemented in efficient platforms and workflows has made massive genotyping available to all fields of biology and medicine. Genetic analyses are no longer dominated by experimental work in laboratories, but rather the interpretation of the resulting data. When billions of data points representing thousands of individuals are available, efficient computational tools are required. The focus of this thesis is on developing models, methods and implementations for such tools.The first theme of the thesis is multi-dimensional scans for quantitative trait loci (QTL) in experimental crosses. By mating individuals from different lines, it is possible to gather data that can be used to pinpoint the genetic variation that influences specific traits to specific genome loci. However, it is natural to expect multiple genes influencing a single trait to interact. The thesis discusses model structure and model selection, giving new insight regarding under what conditions orthogonal models can be devised. The thesis also presents a new optimization method for efficiently and accurately locating QTL, and performing the permuted data searches needed for significance testing. This method has been implemented in a software package that can seamlessly perform the searches on grid computing infrastructures.The other theme in the thesis is the development of adapted optimization schemes for using hidden Markov models in tracing allele inheritance pathways, and specifically inferring haplotypes. The advances presented form the basis for more accurate and non-biased line origin probabilities in experimental crosses, especially multi-generational ones. We show that the new tools are able to reconstruct haplotypes and even genotypes in founder individuals and offspring alike, based on only unordered offspring genotypes. The tools can also handle larger populations than competing methods, resolving inheritance pathways and phase in much larger and more complex populations. Finally, the methods presented are also applicable to datasets where individual relationships are not known, which is frequently the case in human genetics studies. One immediate application for this would be improved accuracy for imputation of SNP markers within genome-wide association studies (GWAS).
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| 4. |
- Nettelblad, Carl
(författare)
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Using Markov models and a stochastic Lipschitz condition for genetic analyses
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A proper understanding of biological processes requires an understanding of genetics and evolutionary mechanisms. The vast amounts of genetical information that can routinely be extracted with modern technology have so far not been accompanied by an equally extended understanding of the corresponding processes.The relationship between a single gene and the resulting properties, phenotype of an individual is rarely clear. This thesis addresses several computational challenges regarding identifying and assessing the effects of quantitative trait loci (QTL), genomic positions where variation is affecting a trait. The genetic information available for each individual is rarely complete, meaning that the unknown variable of the genotype in the loci modelled also needs to be addressed. This thesis contains the presentation of new tools for employing the information that is available in a way that maximizes the information used, by using hidden Markov models (HMMs), resulting in a change in algorithm runtime complexity from exponential to log-linear, in terms of the number of markers. It also proposes the introduction of inferred haplotypes to further increase the power to assess these unknown variables for pedigrees of related genetically diverse individuals. Modelling consequences of partial genetic information are also treated.Furthermore, genes are not directly affecting traits, but are rather expressed in the environment of and in concordance with other genes. Therefore, significant interactions can be expected within genes, where some combination of genetic variation gives a pronounced, or even opposite, effect, compared to when occurring separately. This thesis addresses how to perform efficient scans for multiple interacting loci, as well as how to derive highly accurate empirical significance tests in these settings. This is done by analyzing the mathematical properties of the objective function describing the quality of model fits, and reformulating it through a simple transformation. Combined with the presented prototype of a problem-solving environment, these developments can make multi-dimensional searches for QTL routine, allowing the pursuit of new biological insight.
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