Standard Computational Approaches Research Articles

2-Site versus 3-site models of ATP hydrolysis by F1-ATPase: definitive mathematical proof using combinatorics and conservation equations.

The F1-ATPase enzyme is the smallest-known molecular motor that rotates in 120° steps, driven by the hydrolysis of ATP. It is a multi-subunit enzyme that contains three catalytic sites. A central question is how the elementary chemical reactions that occur in the three sites are coupled to mechanical rotation. Various models and coupling schemes have been formulated in an attempt to answer this question. They can be classified as 2-site (bi-site) models, exemplified by Boyer's binding change mechanism first proposed 50years ago, and 3-site (tri-site) models such as Nath's torsional mechanism, first postulated 25years ago and embellished 1year back. Experimental data collated using diverse approaches have conclusively shown that steady-state ATP hydrolysis by F1-ATPase occurs in tri-site mode. Hence older models have been continually modified to make them conform to the new facts. Here, we have developed a pure mathematical approach based on combinatorics and conservation laws to test if proposed models are 2-site or 3-site. Based on this novel combinatorial approach, we have proved that older and modified models are effectively bi‒site models in that catalysis and rotation in F1-ATPase occurs in these models with only two catalytic sites occupied by bound nucleotide. Hence these models contradict consensus experimental data. The recent 2023 model of ATP hydrolysis by F1-ATPase has been proved to be a true tri-site model based on our novel mathematical approach. Such pure mathematical proofs constitute an important step forward for ATP mechanism. However, in what must be considered an aspect with great scientific potential, the power of such mathematical proofs has not been fully exploited to solve molecular biological problems, in our opinion. We believe that the creative application of pure mathematical proofs (for another example see Nath in Theory Biosci 141:249-260, 2022) can help resolve with finality various longstanding molecular-level issues that arise as a matter of course in the analysis of fundamental biological problems. Such issues have proved extraordinarily difficult to resolve by standard experimental, theoretical, or computational approaches.

Large Roles of Small Proteins.

Bacterial proteins of ≤50 amino acids, denoted small proteins or microproteins, have been traditionally understudied and overlooked, as standard computational, biochemical, and genetic approaches often do not detect proteins of this size. However, with the realization that small proteins are stably expressed and have important cellular roles, there has been increased identification of small proteins in bacteria and eukaryotes. Gradually, the functions of a few of these small proteins are being elucidated. Many interact with larger protein products to modulate their subcellular localization, stabilities, or activities. Here, we provide an overview of these diverse functions in bacteria, highlighting generalities among bacterial small proteins and similarly sized proteins in eukaryotic organisms and discussing questions for future research.

B lymphocytes in treatment-naive paediatric patients with lupus are epigenetically distinct from healthy children

BackgroundSLE is likely triggered by gene–environment interactions. We have shown that most SLE-associated haplotypes encompass genomic regions enriched for epigenetic marks associated with enhancer function in lymphocytes, suggesting genetic risk...

Qualitative Coding in the Computational Era: A Hybrid Approach to Improve Reliability and Reduce Effort for Coding Ethnographic Interviews.

Sociologists have argued that there is value in incorporating computational tools into qualitative research, including using machine learning to code qualitative data. Yet standard computational approaches do not neatly align with traditional qualitative practices. The authors introduce a hybrid human-machine learning approach (HHMLA) that combines a contemporary iterative approach to qualitative coding with advanced word embedding models that allow contextual interpretation beyond what can be reliably accomplished with conventional computational approaches. The results, drawn from an analysis of 87 human-coded ethnographic interview transcripts, demonstrate that HHMLA can code data sets at a fraction of the effort of human-only strategies, saving hundreds of hours labor in even modestly sized qualitative studies, while improving coding reliability. The authors conclude that HHMLA may provide a promising model for coding data sets where human-only coding would be logistically prohibitive but conventional computational approaches would be inadequate given qualitative foci.

A Computational Comparative Study for the Spectroscopic Evaluation of Triazine Derivative Dyes in Implicit Solvation Model Systems Using Semi-Empirical and Time-Dependent Density Functional Theory Approaches

The spectroscopic data for a range of cyclopenta-[d][1,2,3]-triazine derivative dyes have been evaluated using various standard computational approaches. Absorption data of these dyes were obtained using the ZINDO/S semi-empirical model for vertical excitation energies of structures optimised with the AM1, PM3, and PM6 methods. These studies were conducted under vacuum and solution states using the polarisation continuum model (PCM) for implicit solvation in the linear response model. The accuracy, along with the modest computational costs of using the ZINDO/S prediction, combined with the PM3 optimisation method for absorption data was reliable. While a higher computational cost is required for the time-dependent density functional theory (TDDFT), this method offers a reliable method for calculating both the absorption and emission data for the dyes studied (using vertical and adiabatic excitation energies, respectively) via state-specific solvation. This research demonstrates the potential of computational approaches utilising solvation in evaluating the spectroscopic properties of dyes in the rational design of fluorescent probes.

The Biot coefficient for an elasto-plastic material

This paper investigates the influence of the development of elasto-plastic failure on the evolution of the Biot coefficient for a fluid-saturated geomaterial. Attention is restricted to the study of an elasto-plastic porous skeleton that has a solid phase with failure characteristics corresponding to an isotropic medium with a von-Mises-type failure criterion and an associated flow rule. The evolution of the Biot coefficient with the development of failure is illustrated through specific examples. The assumptions implicit in standard computational approaches for examining poroelastic behavior in the light of these developments are also discussed.

Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations

This work introduces a new uncertainty quantification method to better deal with scarce data and long simulation run times in Formula 1 design. Race cars are produced in low quantities and for maximum performance. Thus, their designing process is characterised by manufacturing data shortage and complex Computational Fluid Dynamics simulations with long run times. Their car aerodynamics is subject to many random variables that introduce uncertainty into the down-force and drag performance, such as variations in ride height, front wing direction and pitch angle. To accurately predict the car performance during a race, it is important to study the effect of these random variables. This assessment cannot be performed with the standard deterministic Computational Fluid Dynamics approaches used in Formula 1. Even with regard to stochastic approaches, no efficient method has so far been suggested that addresses the problem of data scarcity. The reason for this is that most efficient uncertainty quantification methods fit probability distributions to the scarce data. It is shown in this work that probability distribution fitting can create a significant error using a simple two-dimensional diffuser example. Subsequently, the use of a new data-driven Polynomial Chaos method and its sparse multi-dimensional extension is suggested and demonstrated for Formula 1 to reduce such errors. This method allows to avoid distribution fitting because it is based on pure data. SAMBA’s general formulation also makes it easier to combine any possible inputs within a sparse description for problems with many variables. SAMBA is applied to two realistic car three-dimensional Computational Fluid Dynamics simulations: a NACA 0012 tip wing and the front part of a Formula 1 car. The probabilistic variations of the lift and drag of these two configurations are calculated using SAMBA and shown to be significant.

Specific Noncovalent Interactions Determine Optimal Structure of a Buried Ligand Moiety: QM/MM and Pure QM Modeling of Complexes of the Small-Molecule CD4 Mimetics and HIV-1 gp120.

The small-molecule CD4 mimetics (smCD4mcs) are a class of highly potent HIV-1 entry inhibitors characterized by a unique structure-activity relationship (SAR). They share a halogenated phenyl ring (region 1) that deeply inserts into an otherwise water-filled cavity at the CD4 binding site on the gp120 surface, the so-called F43 cavity. Conservative modifications to region 1 away from this halogenated phenyl motif have all led to loss of activity, despite the fact that they are predicted by standard empirical computational approaches to bind equally well, making it difficult to further optimize this region of the compounds to increase binding to gp120. In this study we used quantum mechanical methods to understand the roots of the interactions between region 1 and the F43 cavity. We clearly demonstrate the presence of halogen bond/σ-hole and dispersion interactions between region 1 and the F43 cavity residues F376-N377, which are not captured by standard molecular mechanics approaches and the role played by the smCD4mc in the F43 cavity desolvation. These findings rationalize why the halogenated region 1 has proven so difficult to move beyond in smCD4mc optimization, in agreement with experimental evidence.

Does the configurational entropy of polydisperse particles exist?

Classical particle systems characterized by continuous size polydispersity, such as colloidal materials, are not straightforwardly described using statistical mechanics, since fundamental issues may arise from particle distinguishability. Because the mixing entropy in such systems is divergent in the thermodynamic limit, we show that the configurational entropy estimated from standard computational approaches to characterize glassy states also diverges. This reasoning would suggest that polydisperse materials cannot undergo a glass transition, in contradiction to experiments. We explain that this argument stems from the confusion between configurations in phase space and states defined by free energy minima, and propose a simple method to compute a finite and physically meaningful configurational entropy in continuously polydisperse systems. Physically, the proposed approach relies on an effective description of the system as an M*-component system with a finite M*, for which finite mixing and configurational entropies are obtained. We show how to directly determine M* from computer simulations in a range of glass-forming models with different size polydispersities, characterized by hard and soft interparticle interactions, and by additive and non-additive interactions. Our approach provides consistent results in all cases and demonstrates that the configurational entropy of polydisperse system exists, is finite, and can be quantitatively estimated.

Argument-structure and implicational constructions in a knowledge base

This paper provides formalized, machine-tractable representations of two broad kinds of constructional configuration, argument-structure and implicational constructions, on the basis of previous linguistic analyses. It discusses computational implementation requirements on constructional description. In this respect, the paper argues that the Goldbergian approach (cf. Goldberg, 2006) provides the best fit for the implementation of implicational constructions, while a “mini-constructionist” account (cf. Boas, 2014) is suitable for argumentstructure constructions. Because of their representativeness, we have chosen to illustrate our discussion by making reference to the family of English resultatives, which are argumentstructure constructions, and to the family of Wh-attitudinal constructions, which are implicational. Computational implementation demands that the members of the family of the resultative be split into mini-constructions, while the complexity of implicational configurations requires that different formal variants be grouped together under one single computational representation. The paper further makes explicit proposals for the machine tractability of lexical-constructional integration and of meaning implications that have reached constructional status through entrenchment, two problems that remain unsolved within standard computational approaches to language processing.

Categorization of seismic sources by auditory display: A blind test

Recordings of the Earth's surface oscillation as a function of time (seismograms) can be sonified by compressing time so that most of the signal's frequency spectrum falls in the audible range. The pattern-recognition capabilities of the human auditory system can then be applied to the auditory analysis of seismic data. In this experiment, we sonify a set of seismograms associated with a magnitude-5.6 Oklahoma earthquake recorded at 17 broadband stations within a radius of ~300km from the epicenter, and a group of volunteers listen to our sonified seismic data set via headphones. Most of the subjects have never heard a sonified seismogram before. Given the lack of studies on this subject, we prefer to make no preliminary hypotheses on the criteria employed by the listeners: we follow the free categorization approach, asking listeners to simply group sounds that they perceive as similar. We find that listeners tend to group together sonified seismograms sharing one or more underlying physical parameters, including source-receiver distance, source-receiver azimuth, and, possibly, crustal structure between source and receiver and/or at the receiver. This suggests that, if trained to do so, human listeners can recognize subtle features in sonified seismic signals. It remains to be determined whether auditory analysis can complement or lead to improvements upon the standard visual and computational approaches in specific tasks of geophysical interest. HighlightsSeismic data is sonified.The resulting sounds are presented to subjects in a listening task.Subjects are sensitive to some geophysical features.

The not-always-uniquely-predictive power of an evolutionary approach to understanding our not-so-computational nature.

We thank Klasios (2014)1 and Stephen et al. (2014)2 for their commentaries on our paper (Barrett et al., 2014)3. Criticisms like these can only help to improve the quality of arguments offered on both sides. Both Klasios's and Stephen et al.'s commentaries generate misconceptions, however, about the aim of our article and our stated position. Before we respond more generally to their arguments, we want to correct these mistaken impressions. First, Klasios states that we misinterpret the EP notion of computation. This is simply false. We do not argue that EP posits a physical architecture. Our characterization of the EP view (p. 3) is that it “relies heavily on analogies to computational algorithms, functions, inputs, and outputs” and that its research strategy “involves … hypothesizing the kinds of algorithmic “design features” that any psychological adaptation would require in order to solve such a problem.” The notion of a physical architecture was raised in response to Robert Kurzban's implied suggestion that psychological adaptations are analogous to morphological (i.e., physical) adaptations, and can be reverse engineered in the same way. We disputed Kurzban's argument precisely because there are no grounds for positing a particular kind of physical architecture that could serve to support such an analogy (see also Peters, 2013). Second, Stephen et al. (2014) present our argument as stating that the primary contribution of an evolutionary approach to psychology is the idea of massive modularity when, in fact, we stated merely that the modularity of evolved adaptations is the primary distinction between EP and standard computational theories. We do not consider modularity to be the primary contribution of an evolutionary approach to human behavior, as should be clear from our previous work (e.g., Barrett et al., 2001; Pollet et al., 2009; Stulp and Barrett, 2014), as well as our argument in the original paper. Both these misconceptions perhaps arise because of a failure to appreciate that we were addressing the specific question posed for this research topic: is EP the obvious alternative to standard computational approaches to the mind? Our answer was that one evolutionary approach to psychology (i.e., the “Santa Barbara School” of Evolutionary Psychology, which we refer to as EP throughout this piece) was not an alternative approach, nor could it be, precisely because it is a computational theory. As such, it could only be distinguished from other cognitive approaches via the manner in which it applied evolutionary thinking. A large part of our paper was then devoted to why we felt the particular evolutionary approach adopted—namely, modular psychological adaptation—fell short. Other evolutionary approaches are not vulnerable to this criticism, and so we did not include these in our analysis. Thus, nowhere do we dispute Stephen et al.'s main point that evolutionary theory is useful for explaining human behavior and that, using Tinbergen's integrative approach, one can generate unique predictions (see also Barrett and Stulp, 2013).

Evaluation of Aerodynamic Loads via Reduced-Order Methodology

Centroidal Voronoi tessellation, leave-one-out cross validation, proper orthogonal decomposition, and multidimensional interpolation are integrated to define a reduced-order modeling approach for the parametric evaluation of steady aerodynamic loads. The proper orthogonal decomposition-based methodology allows reducing the number of degrees of freedom of the problem while maintaining good accuracy for the solution of complex three-dimensional viscous turbulent flows. As a result, it yields fairly accurate solutions at a fraction of the time required by standard computational fluid dynamics approaches. Three-dimensional examples for fixed- and rotary-wing cases of industrial relevance are used to assess the method in the cases of subsonic and transonic flow conditions.

Abstract 4263: Identifying somatic mutation hotspots across protein family alignments

Abstract In cancer, somatic driver mutations often target “hotspots” of paralogous residues across evolutionarily related members of a single protein family. These hotspots usually confer a dominant oncogenic function, such as constitutive catalytic activity or binding. These hotspots generally represent the most “druggable” genetic alterations. Examples of protein families targeted in such a manner include the Ras and receptor Tyrosine Kinase family. Protein family hotspots are not directly assessed by standard computational approaches for statistically nominating genes under positive somatic selection, such as MutSig, InVex, or MuSic. These methods only assess single genes for statistical enrichment of total mutation burden or the presence of mutation hotspots. As a result, such approaches are only powered to detect protein family hotspots in cases where each (or at least one) protein family member is very frequently mutated (e.g. KRAS, NRAS, HRAS). They are not powered to detect hotspots in highly functionally redundant families, of which each member may be only mutated in <<1% of a tumor type. To directly identify such protein-family hotspots, we have developed a computational framework, MutPfam. This framework superimposes mutation data onto Pfam (http://pfam.sanger.ac.uk/) (protein family and subfamily multiple sequence alignments and identifies statistically significant hotspots. We demonstate our method on protein families involved in the catalysis of small-molecular catabolism using the TCGA Pan-Cancer dataset (https://tcga-data.nci.nih.gov/) comprising somatic mutation calls for 4608 whole-exome sequenced patients and 21 tumor types. As we demonstrate, our framework enables the de novo discovery of mutation hotspots across protein families. Though developed for somatic mutation analysis, this framework could also be applied to the analysis of inherited mutations. Computational issues meriting future investigation include (1) alternate definitions of protein alignments and sub-families (e.g. involving three dimensional structure, biophysical constraints) (2) improved statistical modeling of the local background mutation rate (incorporating local genomic covariates that mediate heterogeneity) (3) incorporation of variant functional impact scores for missense variants. Citation Format: Marcin Imielinski, Charles Du, Matthew Meyerson. Identifying somatic mutation hotspots across protein family alignments. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4263. doi:10.1158/1538-7445.AM2014-4263

From computers to cultivation: reconceptualizing evolutionary psychology.

Does evolutionary theorizing have a role in psychology? This is a more contentious issue than one might imagine, given that, as evolved creatures, the answer must surely be yes. The contested nature of evolutionary psychology lies not in our status as evolved beings, but in the extent to which evolutionary ideas add value to studies of human behavior, and the rigor with which these ideas are tested. This, in turn, is linked to the framework in which particular evolutionary ideas are situated. While the framing of the current research topic places the brain-as-computer metaphor in opposition to evolutionary psychology, the most prominent school of thought in this field (born out of cognitive psychology, and often known as the Santa Barbara school) is entirely wedded to the computational theory of mind as an explanatory framework. Its unique aspect is to argue that the mind consists of a large number of functionally specialized (i.e., domain-specific) computational mechanisms, or modules (the massive modularity hypothesis). Far from offering an alternative to, or an improvement on, the current perspective, we argue that evolutionary psychology is a mainstream computational theory, and that its arguments for domain-specificity often rest on shaky premises. We then go on to suggest that the various forms of e-cognition (i.e., embodied, embedded, enactive) represent a true alternative to standard computational approaches, with an emphasis on “cognitive integration” or the “extended mind hypothesis” in particular. We feel this offers the most promise for human psychology because it incorporates the social and historical processes that are crucial to human “mind-making” within an evolutionarily informed framework. In addition to linking to other research areas in psychology, this approach is more likely to form productive links to other disciplines within the social sciences, not least by encouraging a healthy pluralism in approach.

Abstract A22: Identifying somatic mutation hotspots across protein family alignments.

Abstract In cancer, somatic driver mutations often target “hotspots” of paralogous residues across evolutionarily related members of a single protein family. Mutation hotspots usually confer a dominant oncogenic function and are among the most druggable cancer sequence alterations (e.g. EGFR L858R, BRAF V600E). Examples of protein families targeted in such a manner include the Ras and receptor tyrosine kinase families (1). Protein family hotspots are not directly assessed by standard computational approaches for statistically nominating genes under positive somatic selection in cancer, such as MutSig (2), InVex (3), or MuSic (4). These methods only assess single genes for statistical enrichment of total mutation burden or the presence of mutation hotspots, and thus are only powered to detect protein family hotspots in cases where each (or at least one) protein family member is very frequently mutated (e.g. KRAS, NRAS, HRAS). In particular, they are not powered to detect hotspots in highly functionally redundant families, of which each member may be only mutated in <<1% of cases. To directly identify protein-family hotspots, we have developed a computational framework, MutPfam. This framework superimposes mutation data onto Pfam (http://pfam.sanger.ac.uk/) protein family and subfamily multiple sequence alignments and identifies statistically significant hotspots. We demonstrate our method on the Ras Pfam family (PF00071.17) using TCGA Pan-Cancer dataset (https://tcga-data.nci.nih.gov/) comprising somatic mutation calls for 4608 whole-exome sequenced patients and 21 tumor types. Using phylogeny analysis of this 134-member protein family, we analyzed 110 subfamilies for the presence of significantly mutated hotspots. In addition to canonical KRAS, NRAS, and HRAS hotspots in the switch I (e.g. KRAS G12V, KRAS G13D) and switch II (e.g. HRAS Q61R) domains, we identified several other hotspots achieving significance following Bonferonni correction. Among our top hits was a recently identified melanoma hotspot in RAC1 (3), previously uncharacterized paralogues of a known hotspot in RHOA (RHOA E40Q, RALA D49G, RND1 E48K), and a novel recurrently mutated position in the RAB subfamily (e.g. RAB10 R28C, RAB11B R30H, RAB17 R38W). As we demonstrate, our framework enables the de novo discovery of mutation hotspots across protein families, including those that are rarely mutated in any single family member. Computational issues meriting future investigation include (1) alternate definitions of protein alignments and sub-families (e.g. involving three dimensional structure, biophysical constraints) (2) improved statistical modeling of the local background mutation rate (3) incorporation of variant functional impact scores for missense variants. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A22. Citation Format: Marcin Imielinski, Charles Du, Matthew Meyerson. Identifying somatic mutation hotspots across protein family alignments. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A22.

Validation of Hierarchical Gene Clusters Using Repeated Measurements

Hierarchical clustering is an unsupervised technique, which is a common approach to study protein and gene expression data. In clustering, the patterns of expression of different genes are grouped into distinct clusters, in which the genes in the same cluster are assumed potential to be functionally related or to be influenced by a common upstream factor. Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, the uncertainty in the results obtained is still bothersome. Experimental repetitions are generally performed to overcome the drawbacks of biological variability and technical variability. In this study, the author proposes repeated measurement to evaluate the stability of gene clusters. This paper aims to prove that the stability from the gene clusters, incorporated with repeated measurement, can be used for further analysis.

An siRNA designing tool with a unique functional off-target filtering approach

Investigations have revealed that silencing unwanted transcripts or off-targeting can induce false positive phenotype during RNA interference (RNAi)-based gene function study. But still the standard computational approaches towards small interfering RNA (siRNA) off-target minimization fall short in terms of addressing this false positive phenotype issue. Some of these off-targets may interfere with the biochemical pathway being investigated. It may also inadvertently target cell’s metabolic pathways with unquantifiable consequences on the processes of user’s interest. Here, we report the development of a siRNA selection tool that, for the first time, implements a functional off-target filtering that aims to minimize false positive phenotypes arising from inadvertent targets that are functionally similar or related to the direct target gene, along with a multi-parametric classifier (support vector machine) for optimized selection of potent siRNAs. The functional off-target filtering minimizes the number of off-target genes which are functionally related to the direct target gene, i.e. involved in a common biological process and may have similar phenotype. A text-mining algorithm is used to find related biological processes associated with the direct target and each off-target transcript by comparison of the biological processes associated with these genes. It also gives the user a choice to select one or more off-targets that may be potentially more harmful, from a predicted off-target gene list to be filtered out. Testing with huge set of biologically validated siRNAs from three different sources showed consistent good performance of our tool in terms of effective siRNA selection. It outperformed four potent siRNA selection algorithms of present day in terms of specificity in the selection of highly efficient siRNAs when compared on a common test set. A genome wide testing with potent siRNAs used in high-content screening confirmed validation of 2767 designed siRNAs in terms of phenotypic output. This tool presently supports siRNA designs for human genes and is freely available at http://gyanxet-beta.com.

An Alternative Numerical Solution to a Screw-Thread Problem Using Displacement-Potential Approach

An alternative numerical solution to a screw-thread problem is presented. A displacement-potential-based computational scheme is developed to model the physical conditions and the arbitrary shape of the screw-thread geometry, wherein the principle of linear interpolation is used to transfer the boundary values at the non-nodal boundary points to neighboring field nodal points. The elastic field of a Buttress thread is analyzed to obtain the distributions of displacements and stresses over its normal cross-sections. The solutions are then compared and discussed in light of those obtained by analytical as well as standard computational approaches.

Thermal contact conductance characterization via computational contact homogenization: A finite deformation theory framework

AbstractIn order to predict the macroscopic thermal response of contact interfaces between rough surface topographies, a computational contact homogenization technique is developed at the finite deformation regime. The overall homogenization framework transfers macroscopic contact variables, such as surfacial stretch, pressure and heat flux, as boundary conditions on a test sample within a micromechanical interface testing procedure. An analysis of the thermal dissipation within the test sample reveals a thermodynamically consistent identification for the macroscopic thermal contact conductance parameter that enables the solution of a homogenized thermomechanical contact boundary value problem based on standard computational approaches. The homogenized contact response effectively predicts a temperature jump across the macroscale contact interface. The strong dependence of this homogenized response on macroscale solution variables of interest is demonstrated via representative three‐dimensional numerical investigations. The proposed contact homogenization framework is suitable for the analysis of similar energy transport phenomena across heterogeneous contact interfaces where the investigation of the sources for energy dissipation is of concern. Copyright © 2010 John Wiley & Sons, Ltd.