Evolutionary Scale Research Articles

Direct Numerical Simulation of Nonequilibrium Effects on Decaying Isotropic Turbulence

The Direct Numerical Simulation of decaying isotropic turbulence is performed with nonequilibrium effects. Oxygen is used as the test gas in the simulations. The individual and combined effects of vibrational relaxation and chemical reactions are studied by considering four different simulation cases. The time evolution of mean thermodynamic properties and turbulent statistics is analyzed. Budgets for turbulent kinetic energy and mean internal energy are computed to explain the dynamics of energy exchange associated with different nonequilibrium effects. The time evolution of enstrophy and turbulent length scales is also studied. The dissociation reaction is found to significantly affect the evolution of mean thermodynamic quantities as well as turbulence. Vibrational relaxation is found to significantly affect the flow in the presence of chemical reactions. The effects of changing the initial conditions on the resulting flowfield are analyzed.

A Review of Dendritic Austenite in Cast Irons

Solidification of cast irons usually involves dendritic growth of austenite. This article presents a literature survey about the dendrites in cast irons, their consequences and how they may be manipulated. The literature review is supplemented with relevant micrographs from our research. While austenite usually transforms into ferrite or pearlite, the dendrites limit where liquid flows, where eutectic grows, and where segregated elements go. The amount and shape of dendrites show correlations with tensile strength in pearlitic gray and compacted graphite irons. There are also indications that a coarse dendrite grain structure may be beneficial to tensile strength. The dendrite grain structure depends on melting process parameters and shows sensitivity to melt treatment. The evolution of scale of dendrite arms and their spacing under isothermal condition is by now fairly well-understood; however, work remains to better understand its evolution during cooling and its interaction with the eutectic. The amount and shape of dendrites are less understood in irons of near-eutectic and hypereutectic composition, in particular mixtures of dendrites of distinct scales, associated with regions of distinct graphite morphology. While significant advances have been made in recent years, the role and control of dendrites remain a relatively unexplored area of research with potential to improve production and properties of cast irons.

Multimodal pretraining for unsupervised protein representation learning

Abstract Proteins are complex biomolecules essential for numerous biological processes, making them crucial targets for advancements in molecular biology, medical research, and drug design. Understanding their intricate, hierarchical structures, and functions is vital for progress in these fields. To capture this complexity, we introduce Multimodal Protein Representation Learning (MPRL), a novel framework for symmetry-preserving multimodal pretraining that learns unified, unsupervised protein representations by integrating primary and tertiary structures. MPRL employs Evolutionary Scale Modeling (ESM-2) for sequence analysis, Variational Graph Auto-Encoders (VGAE) for residue-level graphs, and PointNet Autoencoder (PAE) for 3D point clouds of atoms, each designed to capture the spatial and evolutionary intricacies of proteins while preserving critical symmetries. By leveraging Auto-Fusion to synthesize joint representations from these pretrained models, MPRL ensures robust and comprehensive protein representations. Our extensive evaluation demonstrates that MPRL significantly enhances performance in various tasks such as protein–ligand binding affinity prediction, protein fold classification, enzyme activity identification, and mutation stability prediction. This framework advances the understanding of protein dynamics and facilitates future research in the field. Our source code is publicly available at https://github.com/HySonLab/Protein_Pretrain.

Research on Acoustic Emission Characteristics and Crack Evolution during Rock Failure under Tensile and Tensile- and Compressive-Shear Stress States

Tensile, compressive-shear, and tensile-shear failure are three typical failure modes of rock- and lining-support structural materials in underground engineering. Comparative studies of the acoustic emission (AE) evolution characteristics of specimens of the same shape and size under different stress states are of great significance in determining universal disaster warning guidelines. Based on a self-developed multi-functional test system, direct tensile, compressive-shear, and tensile-shear tests were conducted on intact and jointed rock-like specimens, comparing AE amplitude and peak frequency parameters under different failure modes from the perspectives of crack scale and crack type evolution. All failure tests were monitored with microcrack propagation at an early stage until ultimate rupture occurred at the peak-load moment. As a result, the “quiet-period” could only be observed from the compressive-shear test. The AE signals distributed in three bands can be used as an indicator of failure identifications. Tensile and shear cracks can be identified by strong and weak amplitudes of low- and high-frequency signals. These results enhance the knowledge of the failure modes of rock mechanics for more applications in monitoring disasters in rock engineering.

PatchProt: hydrophobic patch prediction using protein foundation models.

Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multitask deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods. In this study, we harnessed a recently released leading large language model Evolutionary Scale Models (ESM-2). Efficient fine-tuning of ESM-2 was achieved by leveraging a recently developed parameter-efficient fine-tuning method. This approach enabled comprehensive training of model layers without excessive parameters and without the need to include a computationally expensive multiple sequence analysis. We explored several related tasks, at local (residue) and global (protein) levels, to improve the representation of the model. As a result, our model, PatchProt, cannot only predict hydrophobic patch areas but also outperforms existing methods at predicting primary tasks, including secondary structure and surface accessibility predictions. Importantly, our analysis shows that including related local tasks can improve predictions on more difficult global tasks. This research sets a new standard for sequence-based protein property prediction and highlights the remarkable potential of fine-tuning foundation models enriching the model representation by training over related tasks. https://github.com/Deagogishvili/chapter-multi-task.

A comprehensive review of the biodiversity of freshwater fish species in Valleys worldwide and in the Kingdom of Saudi Arabia.

An overview of freshwater fish variety worldwide and the variables influencing trends in variation between and within river basins are given in this review. Continental freshwater ecosystems are highly diverse and species-rich, housing nearly 18,000 species of fish (=50% of all fish species) in <0.5% of the total land area and providing a negligible (<0.01%) share of the planet's water supply. Large lowland tropical river basins such as the Amazon, Congo, and Mekong basins are home to the greatest freshwater fish diversity. Freshwater species of fish depth variation at the global magnitude is correlated with the total amount and variation of aquatic habitats and the environment's equilibrium overtime during the evolution of scales. The river continuum concept states that there is a predictable shift in fish species depth, diversity of species, and functional characteristics along gradients of environment from headwater to estuary. The ongoing trade of minerals and organic matter related to nearby floodplains is a strong factor in the number and variety of riverine fishes in most parts of the world (the flood pulse concept). Without coordinated conservation efforts, freshwater fishes will suffer significant losses in abundance and diversity due to the numerous threats they currently face worldwide. However, further development, adaptation, training, and guidance are needed. New technologies based on water conservation, suitable species, and local traditions are needed. Waste materials and local feed additives can also be used. Farmers should be provided with the necessary training and information.

Morphology, Phylogeny, and Evolution of the Rarely Known Genus Admetella McIntosh, 1885 (Annelida, Polynoidae) with Recognition of Four New Species from Western Pacific Seamounts

The polynoid genus Admetella constitutes a deep‐sea assemblage of polychaetes, notable for their large bodies adorned with antennal scales positioned dorsally to the bases of lateral antennae. Furthermore, the genus exhibits swimming proficiencies facilitated by elongated parapodia and flattened chaetae. Despite the frequent encounters with Admetella members during various deep‐sea explorations, a substantial gap in our comprehension of their diversity, phylogeny, and evolutionary trajectories still exists. Our thorough morphological and phylogenetic investigations of specimens obtained from three seamounts located in the tropical western Pacific have unveiled six species belonging to the genus Admetella, four of these being newly identified as Admetella multiseta sp. nov., A. levensteini sp. nov., A. nanhaiensis sp. nov., and A. undulata sp. nov. The other two species of Admetella remain unidentifiable at the species level due to the loss of crucial details. Our phylogenetic analysis, grounded on 13 mitochondrial protein‐coding genes and the inclusion of 12S, 16S, 18S, 28S rRNA, and ITS1–ITS2 genes, substantiates the monophyly of Admetella. Admetella is positioned at an intermediate node within the phylogenetic tree, situated between representative shallow‐water and deep‐sea subfamilies. The independent evolution of antennal scales within Admetella among polynoids constitutes a synapomorphy for this genus. Ancestral state reconstruction (ASR) analyses suggest that deep‐sea polynoids evolved from shallow‐water ancestors that possessed lateral antennae, which were subsequently lost in members inhabiting extreme marine environments, such as deep‐sea hydrothermal vents and anchialine caves. The analysis further implicates that swimming ability independently evolved at least four times within the Polynoidae family.

Investigation of the mechanical work during ultrasonic fatigue loading using pulsed time-resolved X-ray diffraction.

In the energy production and transportation industries, numerous metallic structures may be subjected to at least several billions of cycles, i.e. loaded in the very high cycle fatigue domain (VHCF). Therefore, to design structures in the VHCF domain, a reliable methodology is necessary. One useful quantity to characterize plastic activity at the microscopic scale and fatigue damage evolution is the mechanical work supplied to a material. However, the estimation of this mechanical work in a metal during ultrasonic fatigue tests remains challenging. This paper aims to present an innovative methodology to quantify this. An experimental procedure was developed to estimate the mechanical work from stress and total strain evolution measurements during one loading cycle with a time accuracy of about 50 ns. This was achieved by conducting time-resolved X-ray diffraction coupled to strain gauge measurements at a synchrotron facility working in pulsed mode (single-bunch mode).

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

A Control Method for Improved Fuzzy PID of GSOM for Fish Scale Evolution

A Control Method for Improved Fuzzy PID of GSOM for Fish Scale Evolution

Silver-Rich Clusters Reveal the Initial Size of Nanoscale Network during Dealloying Via Kinetic Monte Carlo Simulation

As residues of the master alloy that survived corrosion, the silver-rich clusters of nanoporous gold (NPG) fabricated by dealloying affect the functional properties of the material. Also, they are representative of the initial nanoscale network established at the beginning of dealloying. The evolution of silver-rich clusters during dealloying at different potentials is investigated via kinetic Monte Carlo simulations [1]. Our simulations show that the dealloying consists of two characteristic processes: (i) the primary process produces an initial three-dimensional bicontinuous network with silver-rich clusters embedded in the ligaments and pure gold covered on ligaments' surface. See Fig.1(a). (ii) the secondary process coarsens the ligaments and further dissolves silver from the ligaments. See Fig.1(b). The size of silver-rich clusters, L Ag, scales with that of ligaments, L lig, during primary dealloying for all studied potentials (Fig.1(c)). Both sizes decrease with increasing dealloying potential. The trends in size and potential are consistent with a Gibbs-Thompson-type relation (dash-dotted line in Fig.1(c)). Yet, the size of silver clusters remains invariant when the ligament size increases during secondary dealloying. Our simulations reveal that the survived clusters provided a way to measure the initial ligament size.Fig.1. Renderings of slicing the NPG structure at different dealloying time (a-b) and the size evolution during primary dealloying (c) (Ref.[1]): (a) the primary dealloying establishes the initial 3D nanoscale network, (b) the secondary dealloying dissolves Ag clusters and coarsens ligaments, (c) inverse of Ag cluster size, L Ag and of ligament size, L lig, during primary dealloying as a function of dealloying potential, Φ.Reference:[1] Y. Li, B.-N. D. Ngô, J. Markmann, J. Weissmüller. Evolution of length scales and of chemical heterogeneity during primary and secondary dealloying, Acta Materialia, (2021) 117424. Figure 1

Factorization of non-global LHC observables and resummation of super-leading logarithms

We present a systematic formalism based on a factorization theorem in soft-collinear effective theory to describe non-global observables at hadron colliders, such as gap-between-jets cross sections. The cross sections are factorized into convolutions of hard functions, capturing the dependence on the partonic center-of-mass energy ŝ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{\\hat{s}} $$\\end{document}, and low-energy matrix elements, which are sensitive to the low scale Q0 ≪ ŝ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{\\hat{s}} $$\\end{document} characteristic of the veto imposed on energetic emissions into the gap between the jets. The scale evolution of both objects is governed by a renormalization-group equation, which we derive at one-loop order. By solving the evolution equation for the hard functions for arbitrary 2 → M jet processes in the leading logarithmic approximation, we accomplish for the first time the all-order resummation of the so-called “super-leading logarithms” discovered in 2006, thereby solving an old problem of quantum field theory. We study the numerical size of the corresponding effects for different partonic scattering processes and explain why they are sizable for pp → 2 jets processes, but suppressed in H/Z and H/Z + jet production. The super-leading logarithms are given by an alternating series, whose individual terms can be much larger than the resummed result, even in very high orders of the loop expansion. Resummation is therefore essential to control these effects. We find that the asymptotic fall-off of the resummed series is much weaker than for standard Sudakov form factors.

Convergent evolution in a large cross-cultural database of musical scales

Scales, sets of discrete pitches that form the basis of melodies, are thought to be one of the most universal hallmarks of music. But we know relatively little about cross-cultural diversity of scales or how they evolved. To remedy this, we assemble a cross-cultural database (Database of Musical Scales: DaMuSc) of scale data, collected over the past century by various ethnomusicologists. Statistical analyses of the data highlight that certain intervals (e.g., the octave, fifth, second) are used frequently across cultures. Despite some diversity among scales, it is the similarities across societies which are most striking: step intervals are restricted to 100-400 cents; most scales are found close to equidistant 5- and 7-note scales. We discuss potential mechanisms of variation and selection in the evolution of scales, and how the assembled data may be used to examine the root causes of convergent evolution.

Multiple axes of visual system diversity in Ithomiini, an ecologically diverse tribe of mimetic butterflies.

The striking structural variation seen in arthropod visual systems can be explained by the overall quantity and spatio-temporal structure of light within habitats coupled with developmental and physiological constraints. However, little is currently known about how fine-scale variation in visual structures arises across shorter evolutionary and ecological scales. In this study, we characterise patterns of interspecific (between species), intraspecific (between sexes) and intraindividual (between eye regions) variation in the visual system of four ithomiine butterfly species. These species are part of a diverse 26-million-year-old Neotropical radiation where changes in mimetic colouration are associated with fine-scale shifts in ecology, such as microhabitat preference. Using a combination of selection analyses on visual opsin sequences, in vivo ophthalmoscopy, micro-computed tomography (micro-CT), immunohistochemistry, confocal microscopy and neural tracing, we quantify and describe physiological, anatomical and molecular traits involved in visual processing. Using these data, we provide evidence of substantial variation within the visual systems of Ithomiini, including: (i) relaxed selection on visual opsins, perhaps mediated by habitat preference, (ii) interspecific shifts in visual system physiology and anatomy, and (iii) extensive sexual dimorphism, including the complete absence of a butterfly-specific optic neuropil in the males of some species. We conclude that considerable visual system variation can exist within diverse insect radiations, hinting at the evolutionary lability of these systems to rapidly develop specialisations to distinct visual ecologies, with selection acting at the perceptual, processing and molecular level.

Application of ultrafiltration technology in drinking water industry of China: A comprehensive assessment of hybrid membrane processes

Abstract Over the last decades, ultrafiltration (UF) has been and will continue to play an important role in the Chinese drinking water industry. The hybrid membrane processes have been emerging as promising alternatives to traditional treatment processes. This review paper offers a thorough overview of the current status of UF technology in the drinking water industry in China, while also evaluating the landscape of different hybrid membrane processes. The paper conducts statistical analysis on the projects operational between 2004 and 2022; and those currently under construction. This analysis accentuates the evolution of scale and capacity, geographical distribution characteristics, and the driving forces. Furthermore, the characteristics and application scenarios of several hybrid membrane processes are emphatically described, including direct UF, gravity-driven membrane process, coagulation-UF, activated carbon-UF, medium-flow process, long-flow process, and double-membrane process. A granular dissection of UF membrane market distribution follows, including an incisive comparison between submerged and pressurized UF membrane systems. Finally, the potential trajectory of UF in the Chinese drinking water industry is prospected. UF applications have grown significantly in China's drinking water industry, with the dominance of medium- and long-flow membrane processes. UF technology will contribute to future decentralized water supply.

Analysis of inflow conditions on the flow past a wall-mounted square cylinder with OpenFOAM

In this study, the flow mechanisms around wall-mounted structures was numerically investigated using Large Eddy Simulation (LES). A comparison was made between the uniform and non-uniform inflow condition to explore the impact of the inflow turbulence on the flow physics, the dynamic response and the hydrodynamic performance of the flow past the cylinder. Sparsity Promoting Dynamic Model Decomposition (SPDMD) was applied to select the dominant modes in the downstream flow field, and to further investigate the evolution of the temporal-spatial scales, as well as the mutual interaction among the wake, the cylinder and the boundary layer. The present study revealed a strong interference between the velocity fluctuations and the wake past the cylinder, in which leads to rapid energy transfer from large eddies to small eddies. Strong convection effects were also observed in the far wake region, where significant interference occurs during the energy transfer induced by fluctuating velocities and the coherent vortex structures. The model analysis successfully identifies the prominent modes of the wake dynamic characteristics under different inflow conditions. This study expands our understanding on the wakes past wall-mounted structures, particularly in terms of their evolution and instability mechanisms, and provides valuable insights for the design and optimization of future wall-mounted structures in engineering practice.

Continental rift asymmetry and segmentation – contributions from the African plate

Prior to the mid-1980s, continental rifts were generally depicted as roughly symmetrical graben, with little complexity along the strike of the basin. In reality, most continental rifts originate as asymmetric, large-scale half graben that are strongly segmented along-strike. This basic architecture does not usually persist unaltered to the rift-to-drift transition but can commonly be observed in both failed continental rift systems and along the passive margins of oceanic plates where rifting succeeded. These geometric aspects of continental rifting have fundamental implications for both hydrocarbon exploration and understanding the causes and processes of continental rupture. Contributions to our understanding of rift asymmetry and segmentation have spanned the past seven decades and come from many settings around the world. However, Africa has played a key role in this undertaking, due to both the presence of well-exposed, active extensional basins in the Afro-Arabian rift system and numerous buried Mesozoic and early Cenozoic rift systems.The first well-documented examples of both rift asymmetry and along-strike segmentation occurred in the Gulf of Suez, northern Egypt, in the 1970s. These early ideas were greatly enhanced and expanded by later studies in both the Western and Eastern branches of the East Africa Rift, including field and refraction/reflection seismic work in Burundi, the Democratic Republic of the Congo, Ethiopia, Kenya, Malawi, Tanzania, and Uganda during the 1980s and 1990s. Asymmetry and segmentation were recognized to be fundamental characteristics of both magmatic and amagmatic rifts, although important differences regarding scale and temporal evolution are evident. At smaller scales, hard-linkage of growing and interacting extensional faults was another key component of subsurface interpretation in the Gulf of Suez by the mid-1970s. Later, outcrop studies in East Africa, supported by subsurface seismic and well data, led to greatly improved models of syn-tectonic sedimentation in step with the advances in understanding of structural geometries. Most recently, concepts derived from the continental margins of other plate settings have been incorporated with the earlier African observations to produce new models of the tectonic evolution of the Red Sea and Gulf of Aden, which in turn has then influenced our overall understanding of the progression from continental to oceanic rifting. This is an ongoing topic of research and discussion that will certainly proceed for many years and require the collaboration of both the international scientific community and local African earth science expertise.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

Assessing protein model quality based on deep graph coupled networks using protein language model.

Model quality evaluation is a crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain a challenge. More importantly, the quality assessment of multimer models is a hot topic for structure prediction. In this study, we propose GraphCPLMQA, a novel approach for evaluating residue-level model quality that combines graph coupled networks and embeddings from protein language models. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality is inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared with the CASP15 residue-level interface evaluation methods among 9108 models in the local residue interface test set of CASP15 multimers. In CAMEO blind test (20 May 2022 to 13 August 2022), GraphCPLMQA ranked first compared with other servers (https://www.cameo3d.org/quality-estimation). GraphCPLMQA also outperforms state-of-the-art methods on 19,035 models in CASP13 and CASP14 monomer test set.

Carrion converging: Skull shape predicts feeding ecology in vultures

AbstractThe link between skull shape and dietary ecology in birds at macroevolutionary scales has recently been called into question by analyses of 3D shape that reveal that cranial anatomy is mainly influenced by other factors such as allometry. It is still unknown whether this form‐function disconnect also exists at smaller evolutionary scales, for example within specialized ecological guilds. Vultures are a diverse guild of 23 extant species in two families (Accipitridae and Cathartidae) that exhibit evolutionary convergence as a result of highly specialized feeding ecology. Vultures are the only known obligate scavengers among vertebrates and are usually grouped together under this single dietary category, but within this specialized diet there are three distinct, species‐specific feeding strategies termed ripper, gulper, and scrapper. We use three‐dimensional geometric morphometrics to quantify the relative contributions of feeding ecology, allometry, and phylogeny on vulture skull shape, along with several non‐vulture raptors of similar size, range and ecology. Families show clear separation in shape, but phylogenetic signal is comparatively weak (Kmult = 0.33). Taking into account the influence of phylogeny, skull shape is not significantly correlated with either skull size or feeding type, but there are examples of strong, significant convergence and parallel shape evolution across feeding groups. Furthermore, skull shape performs strongly in predicting feeding ecology in a phylogenetic discriminant function analysis. These findings highlight the importance of detailed assessment of feeding behavior in studies of ecomorphology, rather than broader dietary categories alone, and reveal that ecology can be readily inferred from form given appropriate information.