Origin and evolution of Atriplex (Amaranthaceae s.l.) in the Americas: Unexpected insights from South American species

With ca. 300 species of herbs, shrubs and subshrubs adapted to saline or alkaline soils, the evolution of the genus Atriplex is key to understand the development of semi-arid environments worldwide. Previous phylogenetic analyses of Atriplex, including only a few species from South America, especially in comparison with North American species represented, proposed a North American origin for the South American Atriplex, through more than one dispersal event. Since South America is one of the four centres of Atriplex diversity, with a high number of endemic species, a wider and more representative sampling of this region is essential to understand the origin and evolution of the genus Atriplex in the Americas. We performed a phylogenetic analysis with estimated clade ages and an ancestral range estimation focused on the American species of Atriplex, to identify South American lineages, their relationships with other lineages of the genus (and particularly with North American ones), and to unravel their biogeographical history in the Americas. Phylogenetic analyses were conducted with sequence data from ITS, ETS and atpB-rbcL spacer markers, using maximum parsimony, Bayesian inference and maximum likelihood approaches. The DEC+J model implemented in BioGeoBEARS was applied in order to infer ancestral ranges. The Americas were colonized by Atriplex in two independent dispersal events: (1) the C4 Atriplex from Eurasia or Australia, and (2) the C3 Atriplex (represented only by the extant A. chilensis) from Eurasia. The C4 American lineage of Atriplex originated roughly 10.4 Ma (95% HPD = 13.31–7.62 Myr) in South America, where two lineages underwent in situ diversification and evolved sympatrically. North America was colonized by Atriplex from South America; later, one lineage moved from North America to South America. Most of the extant species have arisen in the last 3–4 Myr, in Pliocene–Pleistocene. We detected some South American taxa differing in position between both nuclear and atpB-rbcL spacer partitions, which could be explained by chloroplast capture.Fil: Brignone, Nicolás Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Pozner, Raúl Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; ArgentinaFil: Denham, Silvia Suyai. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Botánica Darwinion. Academia Nacional de Ciencias Exactas, Físicas y Naturales. Instituto de Botánica Darwinion; Argentin

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures