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A<sc>bstract</sc> We presentνDoBe, a Python tool for the computation of neutrinoless double beta decay (0νββ) rates in terms of lepton-number-violating operators in the Standard Model Effective Field Theory (SMEFT). The tool can be used for automated calculations of 0νββrates, electron spectra and angular correlations for all isotopes of experimental interest, for lepton-number-violating operators up to and including dimension 9. The tool takes care of renormalization-group running to lower energies and provides the matching to the low-energy effective field theory and, at lower scales, to a chiral effective field theory description of 0νββrates. The user can specify different sets of nuclear matrix elements from various many-body methods and hadronic low-energy constants. The tool can be used to quickly generate analytical and numerical expressions for 0νββrates and to generate a large variety of plots. In this work, we provide examples of possible use along with a detailed code documentation. The code can be accessed through: GitHub:https://github.com/OScholer/nudobe Online User-Interface:https://oscholer-nudobe-streamlit-4foz22.streamlit.app/
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A population‐genomic approach for estimating selection on polygenic traits in heterogeneous environments
Abstract Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on polygenic traits from population genomic time‐series data. I propose a model for environment‐dependent phenotypic selection. The evolutionary genetic consequences of selection are then modelled based on a genotype–phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. The performance of the method when applied to an evolve‐and‐resequence study of host adaptation in the cowpea seed beetle (Callosobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect the causes of (variable) selection to traits and genome‐wide patterns of evolution. Documentation and open‐source computer software (fsabc) implementing this method are available fromgithub(https://github.com/zgompert/fsabc.git).
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- Award ID(s):
- 1844941
- PAR ID:
- 10450852
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 21
- Issue:
- 5
- ISSN:
- 1755-098X
- Page Range / eLocation ID:
- p. 1529-1546
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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