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- Gopalakrishnan, Shyam, et al.
(författare)
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The population genomic legacy of the second plague pandemic
- 2022
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Ingår i: Current Biology. - : Elsevier. - 0960-9822 .- 1879-0445. ; 32:21, s. 4743-4751.e6
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Tidskriftsartikel (refereegranskat)abstract
- Human populations have been shaped by catastrophes that may have left long-lasting signatures in their genomes. One notable example is the second plague pandemic that entered Europe in ca. 1,347 CE and repeatedly returned for over 300 years, with typical village and town mortality estimated at 10%–40%.1 It is assumed that this high mortality affected the gene pools of these populations. First, local population crashes reduced genetic diversity. Second, a change in frequency is expected for sequence variants that may have affected survival or susceptibility to the etiologic agent (Yersinia pestis).2 Third, mass mortality might alter the local gene pools through its impact on subsequent migration patterns. We explored these factors using the Norwegian city of Trondheim as a model, by sequencing 54 genomes spanning three time periods: (1) prior to the plague striking Trondheim in 1,349 CE, (2) the 17th–19th century, and (3) the present. We find that the pandemic period shaped the gene pool by reducing long distance immigration, in particular from the British Isles, and inducing a bottleneck that reduced genetic diversity. Although we also observe an excess of large FST values at multiple loci in the genome, these are shaped by reference biases introduced by mapping our relatively low genome coverage degraded DNA to the reference genome. This implies that attempts to detect selection using ancient DNA (aDNA) datasets that vary by read length and depth of sequencing coverage may be particularly challenging until methods have been developed to account for the impact of differential reference bias on test statistics.
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| 2. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
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| 5. |
- Liu, Jing
(författare)
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Towards Fast and Robust Algorithms in Flash X-ray single-particle Imaging
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern X-ray Free Electron Laser (XFEL) technology provides the possibility to acquire a large number of diffraction patterns from individual biological nano-particles, including proteins, viruses, and DNA. Ideally, the collected data frames are high-quality single-particle diffraction patterns. However, unfortunately, the raw dataset is noisy and also contains patterns with scatterings from multiple particles, contaminated particles, etc. The data complexity and the massive volumes of raw data make pattern selection a time-consuming and challenge task. Further, X-rays interact with particles at random and the captured patterns are the 2D intensities of the scattered waves, i.e. we cannot observe the particle orientations and the phase information from the 2D diffraction patterns. To reconstruct 2D diffraction patterns into 3D structures of the desired particle, we need a sufficiently large single-particle-pattern dataset. The computational methodology for this reconstruction task is still under development and in need of an improved understanding of the algorithmic uncertainties.In this thesis, we tackle some of the challenges to obtain 3D structures of sample molecules from single-particle diffraction patterns. First, we have developed two classification methods to select single-particle diffraction patterns that are similar to provided templates. Second, we have accelerated the 3D reconstruction procedures by distributing the computations among Graphics Processing Units (GPUs) and by proposing an adaptive discretization of 3D space. Third, to better understand the uncertainties of the 3D reconstruction procedure, we have evaluated the impact of the different sources of resolution-limiting factors and introduced a practically applicable computational methodology in the form of bootstrap procedures for assessing the reconstruction uncertainty. These technologies form a data-analysis pipeline for recovering 3D structures from the raw X-ray single-particle data, which also analyzes the uncertainties. With the experimental developments of the X-ray single-particle technology, we expect that the data volumes will be increasing sharply, and hence, we believe such a computational pipeline will be critical to retrieve particle structures in the achievable resolution.
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