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We introduce a wall model for large-eddy simulation (WMLES) applicable to rough surfaces with Gaussian and non-Gaussian distributions for both the transitionally and fully rough regimes. The model is applicable to arbitrary complex geometries where roughness elements are assumed to be underresolved, i.e. subgrid-scale roughness. The wall model is implemented using a multi-hidden-layer feedforward neural network, with the mean geometric properties of the roughness topology and near-wall flow quantities serving as input. The optimal set of non-dimensional input features is identified using information theory, selecting variables that maximize information about the output while minimizing redundancy among inputs. The model also incorporates a confidence score based on Gaussian process modelling, enabling the detection of potentially low model performance for untrained rough surfaces. The model is trained using a direct numerical simulation (DNS) roughness database comprising approximately 200 cases. The roughness geometries for the database are selected from a large repository through active learning. This approach ensures that the rough surfaces incorporated into the database are the most informative, achieving higher model performance with fewer DNS cases compared with passive learning techniques. The performance of the model is evaluated bothaprioriandaposterioriin WMLES of turbulent channel flows with rough walls. Over 550 channel flow cases are considered, including untrained roughness geometries, roughness Reynolds numbers and grid resolutions for both transitionally and fully rough regimes. Our rough-wall model offers higher accuracy than existing models, generally predicting wall shear stress within an accuracy range of 1%–15 %. The performance of the model is also assessed on a high-pressure turbine blade with two different rough surfaces. We show that the new wall model predicts the skin friction and the mean velocity deficit induced by the rough surface on the blade within 1%–10 % accuracy except the region with transition or shock waves. This work extends the building-block flow wall model (BFWM) introduced by Lozano-Durán & Bae (2023.J. Fluid Mech.963, A35) for smooth walls, expanding the BFWM framework to account for rough-wall scenarios. 

Abstract Conflict between genes inherited from the mother (matrigenes) and the father (patrigenes) is predicted to arise during social interactions among offspring if these genes are not evenly distributed among offspring genotypes. This intragenomic conflict drives parent-specific transcription patterns in offspring resulting from parent-specific epigenetic modifications. Previous tests of the kinship theory of intragenomic conflict in honey bees (Apis mellifera) provided evidence in support of theoretical predictions for variation in worker reproduction, which is associated with extreme variation in morphology and behavior. However, more subtle behaviors – such as aggression – have not been extensively studied. Additionally, the canonical epigenetic mark (DNA methylation) associated with parent-specific transcription in plant and mammalian model species does not appear to play the same role as in honey bees, and thus the molecular mechanisms underlying intragenomic conflict in this species is an open area of investigation. Here, we examined the role of intragenomic conflict in shaping aggression in honey bee workers through a reciprocal cross design and Oxford Nanopore direct RNA sequencing. We attempted to probe the underlying regulatory basis of this conflict through analyses of parent-specific RNA m6A and alternative splicing patterns. We report evidence that intragenomic conflict occurs in the context of honey bee aggression, with increased paternal and maternal allele-biased transcription in aggressive compared to non-aggressive bees, and higher paternal allele-biased transcription overall. However, we found no evidence to suggest that RNA m6A or alternative splicing mediate intragenomic conflict in this species. 

Abstract PremiseFloral scent is a complex trait that mediates many plant–insect interactions, but our understanding of how floral scent variation evolves, either independently or in concert with other traits, remains limited. Assessing variation in floral scent at multiple levels of biological organization and comparing patterns of variation in scent to variation in other floral traits can contribute to our understanding of how scent variation evolves in nature. MethodsWe used a greenhouse common garden experiment to investigate variation in floral scent at three scales—within plants, among plants, and among populations—and to determine whether scent, alone or in combination with morphology and rewards, contributes to population differentiation inOenothera cespitosasubsp.marginata. Its range spans most of the biomes in the western United States, such that variation in both the abiotic and biotic environment could contribute to trait variation. ResultsMultiple analytical approaches demonstrated substantial variation among and within populations in compound‐specific and total floral scent measures. Overall, populations were differentiated in morphology and reward traits and in scent. Across populations, coupled patterns of variation in linalool, leucine‐derived compounds, and hypanthium length are consistent with a long‐tongued moth pollination syndrome. ConclusionsThe considerable variation in floral scent detected within populations suggests that, similar to other floral traits, variation in floral scent may have a heritable genetic component. Differences in patterns of population differentiation in floral scent and in morphology and rewards indicate that these traits may be shaped by different selective pressures. 

Driven by a wide range of applications, several principal subspace estimation problems have been studied individually under different structural constraints. This paper presents a uni- fied framework for the statistical analysis of a general structured principal subspace estima- tion problem which includes as special cases sparse PCA/SVD, non-negative PCA/SVD, subspace constrained PCA/SVD, and spectral clustering. General minimax lower and up- per bounds are established to characterize the interplay between the information-geometric complexity of the constraint set for the principal subspaces, the signal-to-noise ratio (SNR), and the dimensionality. The results yield interesting phase transition phenomena concern- ing the rates of convergence as a function of the SNRs and the fundamental limit for consistent estimation. Applying the general results to the specific settings yields the mini- max rates of convergence for those problems, including the previous unknown optimal rates for sparse SVD, non-negative PCA/SVD and subspace constrained PCA/SVD. 

Summary Motivated by the problem of estimating bacterial growth rates for genome assemblies from shotgun metagenomic data, we consider the permuted monotone matrix model $$Y=\Theta\Pi+Z$$ where $$Y\in \mathbb{R}^{n\times p}$$ is observed, $$\Theta\in \mathbb{R}^{n\times p}$$ is an unknown approximately rank-one signal matrix with monotone rows, $$\Pi \in \mathbb{R}^{p\times p}$$ is an unknown permutation matrix, and $$Z\in \mathbb{R}^{n\times p}$$ is the noise matrix. In this article we study estimation of the extreme values associated with the signal matrix $$\Theta$$, including its first and last columns and their difference. Treating these estimation problems as compound decision problems, minimax rate-optimal estimators are constructed using the spectral column-sorting method. Numerical experiments on simulated and synthetic microbiome metagenomic data are conducted, demonstrating the superiority of the proposed methods over existing alternatives. The methods are illustrated by comparing the growth rates of gut bacteria in inflammatory bowel disease patients and control subjects.