The Impact of Cross-Species Gene Flow on Species Tree Estimation

Recent analyses of genomic sequence data suggest cross-species gene flow is common in both plants and animals, posing challenges to species tree estimation. We examine the levels of gene flow needed to mislead species tree estimation with three species and either episodic introgressive hybridization or continuous migration between an outgroup and one ingroup species. Several species tree estimation methods are examined, including the majority-vote method based on the most common gene tree topology (with either the true or reconstructed gene trees used), the UPGMA method based on the average sequence distances (or average coalescent times) between species, and the full-likelihood method based on multilocus sequence data. Our results suggest that the majority-vote method based on gene tree topologies is more robust to gene flow than the UPGMA method based on coalescent times and both are more robust than likelihood assuming a multispecies coalescent (MSC) model with no cross-species gene flow. Comparison of the continuous migration model with the episodic introgression model suggests that a small amount of gene flow per generation can cause drastic changes to the genetic history of the species and mislead species tree methods, especially if the species diverged through radiative speciation events. Estimates of parameters under the MSC with gene flow suggest that African mosquito species in the Anopheles gambiae species complex constitute such an example of extreme impact of gene flow on species phylogeny. [IM; introgression; migration; MSci; multispecies coalescent; species tree.

Spike Effects on Drag Reduction for Hypersonic Lifting Body

A high lift-to-drag ratio is considered crucial for high-altitude and long-endurance hypersonic vehicles. One of the simplest and most useful methods is to install an aerospike in front of the vehicle’s nose. In this paper, the flight aerodynamic characteristics are investigated by simulating and comparing the lifting body with or without the aerospikes at Ma=8. The flowfields around aerospikes using different spike lengths and a hemispherical disk along with the lifting body are analyzed. The results of aerodynamic characteristics indicate that L/D=2 is the best ratio of the spike length to the nose diameter. By comparing with the baseline model, the maximum drag reduction of the nose’s part is 49.3% at α=8  deg using a hemispherical disk. In addition, three shapes of aerospike disks are compared to search for the best disk for hypersonic drag reduction. The best drag reduction is found for the double flat-faced disk aerospike, which gives a pressure drag reduction of 60.5% of the nose’s part at α=8  deg. Furthermore, when the flight angle of attack increases, the drag increases significantly. Employing a certain installation angle is shown to effectively improve the drag reduction around the angle of attack and results in improving the lift-to-drag ratio. At the end, the lift-to-drag ratio of the final optimized design is 9.1% better than that of the baseline model. The pressure center is moved forward by 1.6%, barely influencing the vertical static stability of the vehicle

Prevalence of GB virus type C viraemia in MSM with or without HIV-1 infection in Beijing, China

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Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

Porous energy materials are essential components of many energy devices and systems, the development of which have been long plagued by two main challenges. The first is the ‘curse of dimensionality’, i.e. the complex structure–property relationships of energy materials are largely determined by a high-dimensional parameter space. The second challenge is the low efficiency of optimisation/discovery techniques for new energy materials. Digitalisation of porous energy materials is currently being considered as one of the most promising solutions to tackle these issues by transforming all material information into the digital space using reconstruction and imaging data and fusing this with various computational methods. With the help of material digitalisation, the rapid characterisation, the prediction of properties, and the autonomous optimisation of new energy materials can be achieved by using advanced mathematical algorithms. In this paper, we review the evolution of these computational and digital approaches and their typical applications in studying various porous energy materials and devices. Particularly, we address the recent progress of artificial intelligence (AI) in porous energy materials and highlight the successful application of several deep learning methods in microstructural reconstruction and generation, property prediction, and the performance optimisation of energy materials in service. We also provide a perspective on the potential of deep learning methods in achieving autonomous optimisation and discovery of new porous energy materials based on advanced computational modelling and AI techniques

Plasma microRNA expression profiles in Chinese patients with rheumatoid arthritis

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