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Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.
The study of the genetic structure of cultivated plant populations maintained by farmers is of great importance for evolutionary and conservation biology. Such studies help understand the bases of crop evolution and conservation in relation to farmers' practices. In this study, we assessed the genetic structure underlying landrace diversity of dry-season sorghum. This crop constitutes a historical model of innovation developed by farmers to extend sorghum cultivation to the dry season. Two types of dry-season sorghum are cultivated. We aimed to assess the link between farmers' taxonomy and molecular genetic structure. We collected both types of dry-season sorghum in two villages of northern Cameroon which represented 20 landraces. These landraces were genotyped using eight polymorphic microsatellite markers. This study compared two clustering methods: a Bayesian method (STRUCTURE) which is based on explicit genetic assumptions and the discriminant analysis of principal component method. The latter, more recently proposed, is based on the combination of principal component analysis and discriminant analysis. We noticed a general congruence between these two methods. We also used both methods to infer the genetic structure of our sample. Our results showed strong genetic structuring of the landraces, with K= 14 genetic clusters. We then analysed the fit between farmers' taxonomy and genetic structure. The data suggested that each type and each landrace corresponds to a given genetic entity. This pattern was robust across both villages, despite differences in cultural practices.
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