Ecological speciation in sympatric palms : 3. Genetic map reveals genomic islands underlying species divergence in Howea

Although it is now widely accepted that speciation can occur in the face of continuous gene flow, with little or no spatial separation, the mechanisms and genomic architectures that permit such divergence are still debated. Here, we examined speciation in the face of gene flow in the Howea palms of Lord Howe Island, Australia. We built a genetic map using a novel method applicable to long‐lived tree species, combining it with double digest restriction site–associated DNA sequencing of multiple individuals. Based upon various metrics, we detected 46 highly differentiated regions throughout the genome, four of which contained genes with functions that are particularly relevant to the speciation scenario for Howea, specifically salt and drought tolerance

Rapid parallel adaptation to anthropogenic heavy metal pollution

The impact of human-mediated environmental change on the evolutionary trajectories of wild organisms is poorly understood. In particular, species’ capacities to adapt rapidly (in hundreds of generations or less), reproducibly and predictably to extreme environmental change is unclear. Silene uniflora is predominantly a coastal species, but it has also colonised isolated, disused mines with phytotoxic, zinc-contaminated soils. To test whether rapid, parallel adaptation to anthropogenic pollution has taken place, we used reduced representation sequencing (ddRAD) to reconstruct the evolutionary history of geographically proximate mine and coastal population pairs and found largely independent colonisation of mines from different coastal sites. Furthermore, our results show that parallel evolution of zinc tolerance has occurred without gene flow spreading adaptive alleles between mine populations. In genomic regions where signatures of selection were detected across multiple mine-coast pairs, we identified genes with functions linked to physiological differences between the putative ecotypes, although genetic differentiation at specific loci is only partially shared between mine populations. Our results are consistent with a complex, polygenic genetic architecture underpinning rapid adaptation. This shows that even under a scenario of strong selection and rapid adaptation, evolutionary responses to human activities (and other environmental challenges) may be idiosyncratic at the genetic level and, therefore, difficult to predict from genomic data

Development of a novel system for in-situ repair of aeroengine airfoil via pulsed laser ablation

A novel system for performing in-situ micro-machining of aero-engines for repair applications is proposed in this paper. Using Pulsed Laser Ablation (PLA) as material removal technique, surface cracks caused by foreign objects colliding with airfoils are removed by performing a layer-by-layer micro-machining in the area surrounding the damaged zone. Compared to conventional repair performed by micro-grinding, our invasive PLA system demonstrates a similar level of performance, evaluated from the point of view of fatigue strength in the high cycle (>107) regime. The follow-up fractographic and metallurgical analysis, indicated that although some microstructural characteristics are different between the repair methods, the incurred surface damage is limited in magnitude to a thin surface layer (<30 μm) and the influence on fatigue life is comparable. In the last section, a novel prototype system is presented which allows performing the PLA repair in-situ by use of a miniaturized laser scanning head coupled with a flexible mechanical deployment arm. The system has been successfully tested inside a Roll-Royce Trent jet engine; effectively this proves the possibility of performing in-situ laser micro-machining inside complex mechanical systems such as aero-engines, without the need for complex/expensive disassembly

Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

© 2022, The Author(s), under exclusive licence to Springer Nature Limited.Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.N

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