| 1. |
- Car, Clement, et al.
(författare)
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Population transcriptogenomics highlights impaired metabolism and small population sizes in tree frogs living in the Chernobyl Exclusion Zone
- 2023
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Ingår i: BMC Biology. - : BioMed Central (BMC). - 1741-7007. ; 21
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Tidskriftsartikel (refereegranskat)abstract
- Background: Individual functional modifications shape the ability of wildlife populations to cope with anthropogenic environmental changes. But instead of adaptive response, human-altered environments can generate a succession of deleterious functional changes leading to the extinction of the population. To study how persistent anthropogenic changes impacted local species' population status, we characterised population structure, genetic diversity and individual response of gene expression in the tree frog Hyla orientalis along a gradient of radioactive contamination around the Chernobyl nuclear power plant.Results: We detected lower effective population size in populations most exposed to ionizing radiation in the Chernobyl Exclusion Zone that is not compensated by migrations from surrounding areas. We also highlighted a decreased body condition of frogs living in the most contaminated area, a distinctive transcriptomics signature and stop-gained mutations in genes involved in energy metabolism. While the association with dose will remain correlational until further experiments, a body of evidence suggests the direct or indirect involvement of radiation exposure in these changes.Conclusions: Despite ongoing migration and lower total dose rates absorbed than at the time of the accident, our results demonstrate that Hyla orientalis specimens living in the Chernobyl Exclusion Zone are still undergoing deleterious changes, emphasizing the long-term impacts of the nuclear disaster.
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| 2. |
- Guillemot, Vincent, et al.
(författare)
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A constrained singular value decomposition method that integrates sparsity and orthogonality
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- We propose a new sparsification method for the singular value decomposition—called the constrained singular value decomposition (CSVD)—that can incorporate multiple constraints such as sparsification and orthogonality for the left and right singular vectors. The CSVD can combine different constraints because it implements each constraint as a projection onto a convex set, and because it integrates these constraints as projections onto the intersection of multiple convex sets. We show that, with appropriate sparsification constants, the algorithm is guaranteed to converge to a stable point. We also propose and analyze the convergence of an efficient algorithm for the specific case of the projection onto the balls defined by the norms L1 and L2. We illustrate the CSVD and compare it to the standard singular value decomposition and to a non-orthogonal related sparsification method with: 1) a simulated example, 2) a small set of face images (corresponding to a configuration with a number of variables much larger than the number of observations), and 3) a psychometric application with a large number of observations and a small number of variables. The companion R-package, csvd, that implements the algorithms described in this paper, along with reproducible examples, are available for download from https://github.com/vguillemot/csvd.
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| 3. |
- Löfstedt, Tommy, et al.
(författare)
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Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis
- 2016
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Ingår i: MULTIPLE FACETS OF PARTIAL LEAST SQUARES AND RELATED METHODS. - Cham : SPRINGER INT PUBLISHING AG. - 9783319406435 - 9783319406411 ; , s. 129-139
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Konferensbidrag (refereegranskat)abstract
- Regularized Generalized Canonical Correlation Analysis (RGCCA) extends regularized canonical correlation analysis to more than two sets of variables. Sparse GCCA(SGCCA) was recently proposed to address the issue of variable selection. However, the variable selection scheme offered by SGCCA is limited to the covariance (tau = 1) link between blocks. In this paper we go beyond the covariance link by proposing an extension of SGCCA for the full RGCCA model. (tau epsilon [0; 1]). In addition, we also propose an extension of SGCCA that exploits pre-given structural relationships between variables within blocks. Specifically, we propose an algorithm that allows structured and sparsity-inducing penalties to be included in the RGCCA optimization problem.
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