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Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3–97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/ iqtree. [ABSTRACT FROM AUTHOR]

We investigate incompressible temporally developing turbulent boundary layers for the first time using the One‐Dimensional Turbulence (ODT) model. ODT is spatially fully resolved along the 1D domain, only turbulent advection is represented by stochastic mapping events. We compare the mean and root mean square velocity profiles with the Direct Numerical Simulations (DNS) results for bulk Reynolds numbers, Reb = 500 and 1000 (Reb = Ubd/ν, where ν is the kinematic viscosity, Ub is the velocity provided at the bottom wall and d is the trip wire diameter for experiments). The study demonstrates that ODT has the capability to systematically investigate developing boundary layers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Phylogenetic model selection can be performed using neural networks. • A resnet-18 neural network can be used to determine the model of sequence evolution. • A Bi-LSTM can be used to determine presence of rate heterogeneity and estimate the alpha parameter of the Γ --distribution. • Trees reconstructed using the models resulting from neural network estimates are closer to the ground truth than maximum likelihood trees. • Neural network computation times are constant, thus yielding strongly decreased runtimes for alignments with long sequences and many taxa. Selecting the best model of sequence evolution for a multiple-sequence-alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, with discrimination between models determined by one of several information criteria. This requires tree reconstruction and optimisation which can be computationally expensive. We demonstrate that neural networks can be used to perform model selection, without the need to reconstruct trees, optimise parameters, or calculate likelihoods. We introduce ModelRevelator, a model selection tool underpinned by two deep neural networks. The first neural network, NNmodelfind, recommends one of six commonly used models of sequence evolution, ranging in complexity from Jukes and Cantor to General Time Reversible. The second, NNalphafind, recommends whether or not a Γ -distributed rate heterogeneous model should be incorporated, and if so, provides an estimate of the shape parameter, ɑ. Users can simply input an MSA into ModelRevelator, and swiftly receive output recommending the evolutionary model, inclusive of the presence or absence of rate heterogeneity, and an estimate of ɑ. We show that ModelRevelator performs comparably with likelihood-based methods and the recently published machine learning method ModelTeller over a wide range of parameter settings, with significant potential savings in computational effort. Further, we show that this performance is not restricted to the alignments on which the networks were trained, but is maintained even on unseen empirical data. We expect that ModelRevelator will provide a valuable alternative for phylogeneticists, especially where traditional methods of model selection are computationally prohibitive. [ABSTRACT FROM AUTHOR]

The one-dimensional turbulence (ODT) methodology is extended to include an efficient compressible implementation and a model for capturing shock-induced turbulence is presented. Lignell et al. recently introduced a Lagrangian ODT implementation using an adaptive mesh. As the code operates in the incompressible regime (apart from constant-pressure dilatation) it cannot handle compressibility effects and their interactions with turbulence and chemistry. The necessary algorithmic changes to include compressibility effects are highlighted and our model for capturing shock–turbulence interaction is presented. To validate our compressible solver, we compare results for the Sod shock tube problem against a finite volume Riemann solver. To validate our model for shock–turbulence interaction, we present comparisons for a non-reactive and a reactive case. First, results of a shock traveling from light (air) to heavy (SF 6 ) with reshock have been simulated to match mixing width growth data of experiments and turbulent kinetic energy results from LES. Then, for one-step chemistry calibrated to represent an acetylene/air mixture we simulate the interaction of a shock wave with an expanding flame front, and compare results with 2D simulation (2D-sim) data for flame brush formation and ensuing deflagration-to-detonation transitions (DDT). Results for the Sod shock tube comparison show that the shock speed and profile are captured accurately. Results for the non-reactive shock–reshock problem show that interface growth at all simulated Mach numbers is captured accurately and that the turbulent kinetic energy agrees in order of magnitude with LES data. The reactive shock tube results show that the flame brush thickness compares well to 2D-sim data and that the approximate location and timing of the DDT can be captured. The known sensitivity of DDT characteristics to details of individual flow realizations, seen also in ODT, implies that model agreement can be quantified only by comparing flow ensembles, which are presently unavailable other than in an ODT run-to-run sensitivity study that is reported herein. [ABSTRACT FROM AUTHOR]

Friction factor enhancements in pipe-flows subject to fixed electric fields are evaluated using the stochastic One-Dimensional Turbulence (ODT) model. To that extent, low Mach asymptotics are used to derive the leading order effects of the governing equations in electrohydrodynamic (EHD) flows considering low mobility ratios. Temporal and spatial ODT formulations are then carried out for three different Reynolds numbers (Re = 3000 , Re = 4000 and Re = 5000) at a set of electrohydrodynamic numbers in the range 0 ≤ N EHD ≤ 600. The ODT simulation results are compared to published experimental data on Darcy friction factor enhancements of Nelson et al. (1990). The results signalize the importance of the developing flow for the EHD-enhanced drag. On one hand, the ratio N EHD 2/Re2 characterizes the enhancement. On the other hand, the developing flow and the laminar-to-turbulent transition also have an important contribution to the friction factor. The ODT results presented here show that reduced order models can deliver relevant insights in otherwise numerically challenging flow simulations, therefore aiding the quantitative analysis of experimental observations in EHD flows. [ABSTRACT FROM AUTHOR]

The one-dimensional turbulence (ODT) model is applied to a reactant-to-product counterflow configuration and results are compared with DNS data. The model employed herein solves conservation equations for momentum, energy, and species on a one dimensional (1D) domain corresponding to the line spanning the domain between nozzle orifice centers. The effects of turbulent mixing are modeled via a stochastic process, while the Kolmogorov and reactive length and time scales are explicitly resolved and a detailed chemical kinetic mechanism is used. Comparisons between model and DNS results for spatial mean and root-mean-square (RMS) velocity, temperature, and major and minor species profiles are shown. The ODT approach shows qualitatively and quantitatively reasonable agreement with the DNS data. Scatter plots and statistics conditioned on temperature are also compared for heat release rate and all species. ODT is able to capture the range of results depicted by DNS. However, conditional statistics show signs of underignition. [ABSTRACT FROM AUTHOR]

The interaction of a wing/blade profile with turbulent inflow is one of the main sources of sound generation in turbomachines. There are already several noise prediction methodologies available which either appear not to account for the influence of geometrical and flow parameters on noise generation or have been able to account for a remarkably limited extent due to the requirement of high-performance computing for flow calculations. This leads to the motivation of the current paper, which presents a low-cost and easy-to-use noise prediction methodology based on the statistical modelling of the inflow turbulence and Lookup Table (LUT) approach for aeroacoustic design and optimisation. The development of the statistical method is divided into three parts; namely – i) calculating the background flow, ii) modelling of statistically optimised inflow disturbance, iii) computing the far-field sound pressure for individual vortex passages and superpose them linearly – this step involves repeated computation of identical vortex passages and can be therefore easily sped up using a database approach. In the framework of this work, a new approach to model the inflow turbulence using vortex particles characterised by shape functions, based on waveforms, is presented. The idea is to not conduct a time-dependent unsteady calculation of the flow field in real-time, instead to consider the mean flow around the profile in the computational domain, in which the vortex particles are convected to realise a statistical turbulent signal. The convection of these vortex particles, also, does not take place in the real-time calculation, instead, vortices of every possible size and strength are convected in a similar domain with a specific airfoil, and the acoustic radiation due to their interaction with the airfoil are computed and stored in a database. The far-field noise is predicted using Curle's formulation. The generated database is accessed using the LUT approach to rapidly extract the acoustic signals. Through this approach, the influence of geometrical as well as flow parameters on the noise generated by airfoils can be quantified without requiring to conduct a numerical simulation every time for a new set of geometrical and flow variables. In the article, the application of the method for different blade profiles is shown, and the results obtained are compared with the standard literature. [ABSTRACT FROM AUTHOR]

• We introduce an extension of the spatial ODT formulation (S-ODT), in order to study a variable density internal air flow. • S-ODT results are compared to the results of a traditional temporal ODT formulation (T-ODT) and to reference DNS data. • For bulk quantities, both the T-ODT and S-ODT formulations have a similar performance in comparison to DNSs. • For the evaluation of radial distributions, the S-ODT formulation clearly outperforms the T-ODT formulation. A novel spatial formulation of the One-Dimensional Turbulence (ODT) model is applied to a vertical pipe-flow with heat transfer, analogous to the Direct Numerical Simulation (DNS) performed by Bae et al. [Phys. Fluids 18, (075102) (2006)]. The framework presented here is an extension for radially confined domains of the cylindrical ODT spatial formulation for low Mach number flows with variable density. The variable density simulations for air (Prandtl number P r = 0.71) are performed at an initial bulk Reynolds number R e b , 0 , D N S = 6000 and Grashof number G r 0 , D N S = 6.78 × 10 6. ODT results are presented for both the spatial formulation introduced in this work and the standard temporal formulation for cylindrical flows introduced by Lignell et al. [Theor. Comput. Fluid Dyn. 32, 4 (2018), pp. 495–20]. Streamwise bulk profiles and radial profiles at specific streamwise positions for the temporal and spatial formulations are in good agreement with the DNS results from Bae et al. For the present application, the spatial formulation yields physically better results in comparison to the temporal formulation. Overall, the findings in the original work of Bae et al. were corroborated with ODT. Although the framework proposed in this work is not a compressible framework and has some clear limitations regarding conservation properties, we suggest its use for future studies in the low Mach number variable density regime. [ABSTRACT FROM AUTHOR]

Modeling turbulent mixing is a standing challenge for nonpremixed chemically reacting flows. Key complications arise from the requirement to capture all relevant scales of the flow and the necessity to distinguish between turbulent advective transport and molecular diffusive transport processes. In addition, anisotropic mean shear, variable advection time scales, and the coexistence of turbulent and nonturbulent regions need to be represented. The fundamental issues at stake are addressed by investigating multi-scalar mixing in a three-stream coaxial jet with a map-based stochastic one-dimensional turbulence model. ODT provides full-scale resolution at affordable costs by a radical reduction of complexity compared to high-fidelity Navier–Stokes solvers. The approach is partly justified by an application of the boundary-layer approximation, but neglects fluctuating axial pressure gradients. It is demonstrated that low-order scalar statistics are reasonably but not fully captured. Despite this shortcoming, it is shown that the model is able to reproduce experimental state-space statistics of multi-stream multi-scalar mixing. The model therefore offers physics-compatible improvements in multi-stream mixing modeling despite some fundamental limitations that remain from unjustified assumptions. [Display omitted] • Map-based stochastic modeling of multi-stream mixing with full-scale resolution. • Comprehensive assessment of spatial sensitivities of scalar joint probabilities. • State-space statistics of scalar mixing in shear turbulence are reproduced. • Scalar statistics are obtained diagnostically without closure assumptions. • Local advection velocities and turbulent/nonturbulent regions are represented. [ABSTRACT FROM AUTHOR]

In bilaterians, three orthogonal body axes define the animal form, with distinct anterior-posterior, dorsal-ventral and left-right asymmetries. The key signalling factors are Wnt family proteins for the anterior-posterior axis, Bmp family proteins for the dorsal-ventral axis and Nodal for the left-right axis. Cnidarians, the sister group to bilaterians, are characterized by one oral-aboral body axis, which exhibits a distinct biradiality of unknown molecular nature. Here we analysed the biradial growth pattern in the radially symmetrical cnidarian polyp Hydra, and we report evidence of Nodal in a pre-bilaterian clade. We identified a Nodal-related gene (Ndr) in Hydra magnipapillata, and this gene is essential for setting up an axial asymmetry along the main body axis. This asymmetry defines a lateral signalling centre, inducing a new body axis of a budding polyp orthogonal to the mother polyp's axis. Ndr is expressed exclusively in the lateral bud anlage and induces Pitx, which encodes an evolutionarily conserved transcription factor that functions downstream of Nodal. Reminiscent of its function in vertebrates, Nodal acts downstream of β-Catenin signalling. Our data support an evolutionary scenario in which a 'core-signalling cassette' consisting of β-Catenin, Nodal and Pitx pre-dated the cnidarian-bilaterian split. We presume that this cassette was co-opted for various modes of axial patterning: for example, for lateral branching in cnidarians and left-right patterning in bilaterians. [ABSTRACT FROM AUTHOR]

Replies to comments on the authors' article focused on the genetic interaction between Dobrava virus (DOBV) and Saaremaa virus (SAAV). Description of the phylogenetic aspects of the hantavirus species DOBV; Significance of the occurrence of genetic exchange in the evolution of the DOBV species; Denial of any claim that the proposed recombination events are currently occurring.

Transcription factors are crucial drivers of cellular differentiation during animal development and often share ancient evolutionary origins. The T-box transcription factor Brachyury plays a pivotal role as an early mesoderm determinant and neural repressor in vertebrates; yet, the ancestral function and key evolutionary transitions of the role of this transcription factor remain obscure. Here, we present a genome-wide target-gene screen using chromatin immunoprecipitation sequencing in the sea anemone Nematostella vectensis, an early branching non-bilaterian, and the sea urchin Strongylocentrotus purpuratus, a representative of the sister lineage of chordates. Our analysis reveals an ancestral gene regulatory feedback loop connecting Brachyury, FoxA and canonical Wnt signalling involved in axial patterning that predates the cnidarian-bilaterian split about 700 million years ago. Surprisingly, we also found that part of the gene regulatory network controlling the fate of neuromesodermal progenitors in vertebrates was already present in the common ancestor of cnidarians and bilaterians. However, while several endodermal and neuronal Brachyury target genes are ancestrally shared, hardly any of the key mesodermal downstream targets in vertebrates are found in the sea anemone or the sea urchin. Our study suggests that a limited number of target genes involved in mesoderm formation were newly acquired in the vertebrate lineage, leading to a dramatic shift in the function of this ancestral developmental regulator., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Climate change is causing arctic regions to warm disproportionally faster than those at lower latitudes, leading to alterations in carbon and nitrogen cycling, and potentially higher greenhouse gas emissions. It is thus increasingly important to better characterize the microorganisms driving arctic biogeochemical processes and their potential responses to changing conditions. Here, we describe a novel thaumarchaeon enriched from an arctic soil, Candidatus Nitrosocosmicus arcticus strain Kfb, which has been maintained for seven years in stable laboratory enrichment cultures as an aerobic ammonia oxidizer, with ammonium or urea as substrates. Genomic analyses show that this organism harbors all genes involved in ammonia oxidation and in carbon fixation via the 3-hydroxypropionate/4-hydroxybutyrate cycle, characteristic of all AOA, as well as the capability for urea utilization and potentially also for heterotrophic metabolism, similar to other AOA. Ca . N. arcticus oxidizes ammonia optimally between 20 and 28°C, well above average temperatures in its native high arctic environment (-13-4°C). Ammonia oxidation rates were nevertheless much lower than those of most cultivated mesophilic AOA (20-45°C). Intriguingly, we repeatedly observed apparent faster growth rates (based on marker gene counts) at lower temperatures (4-8°C) but without detectable nitrite production. Together with potential metabolisms predicted from its genome content, these observations indicate that Ca . N. arcticus is not a strict chemolithotrophic ammonia oxidizer and add to cumulating evidence for a greater metabolic and physiological versatility of AOA. The physiology of Ca . N. arcticus suggests that increasing temperatures might drastically affect nitrification in arctic soils by stimulating archaeal ammonia oxidation.