Subdomain Level Research Articles

Reused Protein Segments Linked to Functional Dynamics.

Protein space is characterized by extensive recurrence, or "reuse," of parts, suggesting that new proteins and domains can evolve by mixing-and-matching of existing segments. From an evolutionary perspective, for a given combination to persist, the protein segments should presumably not only match geometrically but also dynamically communicate with each other to allow concerted motions that are key to function. Evidence from protein space supports the premise that domains indeed combine in this manner; we explore whether a similar phenomenon can be observed at the sub-domain level. To this end, we use Gaussian Network Models (GNMs) to calculate the so-called soft modes, or low-frequency modes of motion for a dataset of 150 protein domains. Modes of motion can be used to decompose a domain into segments of consecutive amino acids that we call "dynamic elements", each of which belongs to one of two parts that move in opposite senses. We find that, in many cases, the dynamic elements, detected based on GNM analysis, correspond to established "themes": Sub-domain-level segments that have been shown to recur in protein space, and which were detected in previous research using sequence similarity alone (i.e. completely independently of the GNM analysis). This statistically significant correlation hints at the importance of dynamics in evolution. Overall, the results are consistent with an evolutionary scenario where proteins have emerged from themes that need to match each other both geometrically and dynamically, e.g. to facilitate allosteric regulation.

From Domain Decomposition to Model Reduction for Large Nonlinear Structures

The numerical simulation of multiscale and multiphysics problems requires efficient tools for spatial localization and model reduction. A general strategy combining Domain Decomposition and Nonuniform Transformation Field Analysis (NTFA) is proposed herein for the simulation of nuclear fuel assemblies at the scale of a full nuclear reactor. The model at subdomain level solves the full elastic problem but with a reduced nonlinear loading, based on simplified boundary conditions, reduced creep flow rules, projected sign preserving contact conditions, and a NTFA like reduced friction law to get the evolution of each slipping mode. With this loading reduction, the local solution can be explicitly obtained from a small set of precomputed elementary elastic solutions. The numerical tests indicate that considerable cost reduction (a factor of 50 to 1000) can be achieved while preserving engineering accuracy.

Multiscale sampling for the inverse modeling of partial differential equations

We are concerned with a novel Bayesian statistical framework for the characterization of natural subsurface formations, a very challenging task. Because of the large dimension of the stochastic space of the prior distribution in the framework, typically a dimensional reduction method, such as a Karhunen-Leove expansion (KLE), needs to be applied to the prior distribution to make the characterization computationally tractable. Due to the large variability of properties of subsurface formations (such as permeability and porosity) it may be of value to localize the sampling strategy so that it can better adapt to large local variability of rock properties.In this paper, we introduce the concept of multiscale sampling to localize the search in the stochastic space. We combine the simplicity of a preconditioned Markov Chain Monte Carlo method with a new algorithm to decompose the stochastic space into orthogonal subspaces, through a one-to-one mapping of the subspaces to subdomains of a non-overlapping domain decomposition of the region of interest. The localization of the search is performed by a multiscale blocking strategy within Gibbs sampling: we apply a KL expansion locally, at the subdomain level. Within each subdomain, blocking is applied again, for the sampling of the KLE random coefficients.The effectiveness of the proposed framework is tested in the solution of inverse problems related to elliptic partial differential equations arising in porous media flows. We use multi-chain studies in a multi-GPU cluster to show that the new algorithm clearly improves the convergence rate of the preconditioned MCMC method. Moreover, we illustrate the importance of a few conditioning points to further improve the convergence of the proposed method.

An overlapping domain decomposition method for the solution of parametric elliptic problems via proper generalized decomposition

A non-intrusive proper generalized decomposition (PGD) strategy, coupled with an overlapping domain decomposition (DD) method, is proposed to efficiently construct surrogate models of parametric linear elliptic problems. A parametric multi-domain formulation is presented, with local subproblems featuring arbitrary Dirichlet interface conditions represented through the traces of the finite element functions used for spatial discretization at the subdomain level, with no need for additional auxiliary basis functions. The linearity of the operator is exploited to devise low-dimensional problems with only few active boundary parameters. An overlapping Schwarz method is used to glue the local surrogate models, solving a linear system for the nodal values of the parametric solution at the interfaces, without introducing Lagrange multipliers to enforce the continuity in the overlapping region. The proposed DD-PGD methodology relies on a fully algebraic formulation allowing for real-time computation based on the efficient interpolation of the local surrogate models in the parametric space, with no additional problems to be solved during the execution of the Schwarz algorithm. Numerical results for parametric diffusion and convection–diffusion problems are presented to showcase the accuracy of the DD-PGD approach, its robustness in different regimes and its superior performance with respect to standard high-fidelity DD methods.

Empirical Interscale Finite Element Method (EIFEM) for modeling heterogeneous structures via localized hyperreduction

This work proposes a special type of Finite Element (FE) technology – the Empirical Interscale FE method – for modeling heterogeneous structures in the small strain regime, for both dynamic and static analyses. The method combines a domain decomposition framework, where interface conditions are established through “fictitious” frames, with dimensional hyperreduction at subdomain level. Similar to other multiscale FE methods, the structure is assumed to be partitioned into coarse-scale elements, each of these elements is equipped with a fine-scale subgrid, and the displacements of the boundaries of the coarse-scale elements are described by standard polynomial FE shape functions. The distinguishing feature of the proposed method is the employed “interscale” variational formulation, which directly relates coarse-scale nodal internal forces with fine-scale stresses, thereby avoiding the typical nested local/global problems that appear, in the nonlinear regime, in other multiscale methods. This distinctive feature, along with hyperreduction schemes for nodal internal and external body forces , greatly facilitate the implementation of the proposed formulation in existing FE codes for solid elements. Indeed, one only has to change the location and weights of the integration points, and to replace a few polynomial-based FE matrices with “empirical” operators, i.e., derived from the information obtained in appropriately chosen computational experiments. We demonstrate that the elements resulting from this formulation are not afflicted by volumetric locking when dealing with nearly-incompressible materials, and that they can handle non-matching fine-scale grids as well as curved structures. Last but not least, we show that, for periodic structures, this method converges upon mesh refinement to the solution delivered by classical first-order computational homogenization. Thus, although the method does not presuppose scale separation, it can represent solutions in this limiting case by taking sufficiently small coarse-scale elements.

Psychosocial Outcomes in Autistic Children Before and During the COVID-19 Pandemic.

Studies on the impact of the COVID-19 pandemic on autistic children's psychosocial outcomes have shown mixed results. In the current study we aimed to gain a better insight into the effect of the COVID-19 pandemic by comparing psychosocial outcomes collected pre-pandemic with data collected during the pandemic. We used the Strengths and Difficulties Questionnaire (SDQ) to examine change over time in psychosocial outcomes of autistic children from pre-pandemic (T0) to lockdown I (T1) and lockdown II (T2) in the Netherlands. We expected a deterioration in psychosocial outcomes. There were 224 participants in T0 and T1, of which 141 also participated in T2. The results showed a surprising improvement in psychosocial outcomes from T0 to T1. Special education and female gender were associated with increased difficulties over time, while higher age was associated with decreased difficulties. At the subdomain level we found that emotional problems remained stable, while hyperactivity, conduct problems, and peer problems decreased, and prosocial behavior increased. Attending special education predicted increased peer problems over time, while higher age predicted both decreased conduct problems and increased prosocial behavior over time. The COVID-19 pandemic may have temporarily improved the fit between the psychosocial needs and the environment for children with autism in the Netherlands.

Vessel Contour Detection in Intracoronary Images via Bilateral Cross-Domain Adaptation.

Vessel contour detection (VCD) in intravascular images is important for the quantitative assessment of vessels. However, it is still a challenging task due to a high degree of morphology variability. Images from a single modality lack sufficient information on the vessel morphology due to the natural limitation of the imaging capability. Therefore, the single-modality VCD methods have difficulty extracting sufficient morphological information. Cross-modality methods have the potential to overcome morphology variability by extracting more information from different modalities. However, they still face the difficulty of the domain discrepancy, i.e. feature space discrepancy and label space inconsistency. In this paper, we aims to address this domain discrepancy for VCD. To overcome label space inconsistency, our method divides the label space into private label space and shared label space. It constructs subdomains for the private label space and the shared label space, and then minimizes the task risk at the subdomain level. To overcome feature space discrepancy, it extracts domain-invariant features via domain adaptation between the subdomains. Finally, it uses the domain-invariant features as auxiliary information for each subdomain. Extensive experiments on 130 IVUS sequences (135663 images) and 124 OCT sequences (39857 images) show that our method is effective (e.g., the Dice index [Formula: see text] 0.949), and superior to the nineteen state-of-the-art VCD methods.

StackCPA: A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features

Accurately predicting compound-protein binding affinity is a crucial task in drug discovery. Computational models offer the advantages of short time, low cost and safety compared to traditional drug development. Pocket is the key binding region of the protein, which provides invaluable information for drug repositioning and drug design. In this study, we propose an ensemble learning model, called StackCPA, to predict the compound-protein binding affinity. The model integrates multi-scale features of protein pocket and compound through a transfer learning strategy. The protein pocket is described in a fine-grained way by atomic level, residue level and subdomain level. The proposed model StackCPA is evaluated on three binding affinity benchmark datasets. The experiment results show that StackCPA achieves the best performance on all the three datasets in comparison with other state-of-the-art deep learning models. The ablation study shows that the protein pocket can provide sufficient information for affinity prediction and its multi-scale features enable the model to further improve the prediction performance. In addition, the case study for epidermal growth factor receptor erbB1 (EGFR) indicates that StackCPA could serve as an effective tool for drug repurposing. Source codes and data of StackCPA are available at https://github.com/CSUBioGroup/StackCPA.

The Importance of Measuring Mental Wellbeing in the Context of Psychological Distress: Using a Theoretical Framework to Test the Dual-Continua Model of Mental Health.

In total, 2065 participants (female n = 1193; 57.8%) completed two online assessments (minimum 30 days apart) measuring psychological distress, mental wellbeing, and demographic information. In total, 1.1% of participants experienced high distress as well as mental wellbeing confirming that psychological distress and mental wellbeing exist independently (Criterion 1). Bipolarity (Criterion 2) was partly disconfirmed: mental wellbeing consistently decreased as symptom severity increased for depression; however, anxiety and stress did not meet bipolarity requirements. Functional independence (Criterion 3) was established: longitudinal analysis found that participants reliably and simultaneously increased (2.7%) or decreased (4.2%) in distress and mental wellbeing, while cross-sectional analysis showed that psychological distress only explained 38% of the variance in mental wellbeing. Findings provide further support for the dual-continua model through analysis of the proposed assessment criteria, suggesting a further need to measure the dual-continua model at the subdomain level, e.g., depression, anxiety, and stress, as opposed to global psychological distress. Validation for the proposed assessment criteria provides important methodological foundations for future studies.

Subdomain Adaptation Capsule Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear.

Deep learning methods, especially convolutional neural networks (CNNs), have achieved good results in the partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) in the laboratory. However, the relationship of features ignored in CNNs and the heavy dependance on the amount of sample data make it difficult for the model developed in the laboratory to achieve high-precision, robust diagnosis of PD in the field. To solve these problems, a subdomain adaptation capsule network (SACN) is adopted for PD diagnosis in GIS. First, the feature information is effectively extracted by using a capsule network, which improves feature representation. Then, subdomain adaptation transfer learning is used to accomplish high diagnosis performance on the field data, which alleviates the confusion of different subdomains and matches the local distribution at the subdomain level. Experimental results demonstrate that the accuracy of the SACN in this study reaches 93.75% on the field data. The SACN has better performance than traditional deep learning methods, indicating that the SACN has potential application value in PD diagnosis of GIS.

Subdomain adaptation capsule network for unsupervised mechanical fault diagnosis

Domain adaptation is one of the mainstream deep transfer learning strategies to deal with unsupervised fault diagnosis issues. Nevertheless, the existing domain adaptation based diagnosis approaches mainly focus on the alignment of source domain and target domain at global level, which may lead to the confusion between subdomains and finally restrict the improvement of target domain accuracy. In this work, the subdomain adaptation capsule network (SACNet) is developed, and the contributions of the proposed method mainly reflected in the following aspects. Firstly, in the proposed architecture, a novel distance metrics named local joint maximum mean discrepancy (LJMMD) is developed and embedded as part of the objective function. Utilizing the LJMMD, the joint distribution of feature and prediction space of target domain can be matched with that of source domain at subdomain level, which can avoid the limitations of global or single marginal distributions match. Secondly, the capsule network with vector-output is substituted for the classical fully connected adaption layer. Instead of matching specific values, the module value of the capsule vector is set as the object of domain adaption. Thus, the discrepancy between different domains can be admitted to a certain extent, which can endow the model with more excellent generalization ability. The experimental results indicate that, compared with other methods, the suggested LJMMD can achieve higher diagnostic accuracy in most transfer tasks. Besides, the diagnostic accuracy of the developed SACNet can be further improved by nearly 2% after adopting the capsule adaptation layer.

Domain Decomposition-Based Discontinuous Galerkin Time-Domain Method With Weighted Laguerre Polynomials

To further improve the computational efficiency of the existing wave equation-based discontinuous Galerkin time-domain (DGTD) method for modeling electrically small structures, the weighted Laguerre polynomials are incorporated to span the fields in the temporal domain, which finally results in a set of recursive linear equations related to the Laguerre polynomials to be solved. Compared with conventional time integration methods, the proposed approach requires much fewer iterations and thus shows considerable improvement in terms of computational efficiency. With respect to the first-order Maxwell’s curl equation-based DGTD method with weighted Laguerre polynomials, the proposed method reduces the unknowns by more than a half and gives a lighter linear system for solution. In addition, block iterative solvers and the block lower-diagonal-upper (LDU) direct solver can be applied to facilitating the solution at the subdomain level. Three numerical examples are included to demonstrate the accuracy and efficiency of the proposed method. With the weighted Laguerre polynomials, the wave equation-based DGTD method is expected to be more efficient for electrically small structure modeling.

Universal Architectural Concepts Underlying Protein Folding Patterns.

What is the architectural “basis set” of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a dictionary of 1,493 substructures—called concepts—typically at a subdomain level, based on an unbiased subset of known protein structures. Each concept represents a topologically conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the Protein Data Bank and completely inventoried all the concept instances. This yields many insights, including correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence–structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click), provides access to and navigation of the entire dictionary of concepts and their usages, and all associated information. This report is part of a continuing programme with the goal of elucidating fundamental principles of protein architecture, in the spirit of the work of Cyrus Chothia.

Functionally Graded Cellular Structure Design Using the Subdomain Level Set Method with Local Volume Constraints

Functional graded cellular structure (FGCS) usually shows superior mechanical behavior with low density and high stiffness. With the development of additive manufacturing, functional graded cellular structure gains its popularity in industries. In this paper, a novel approach for designing functionally graded cellular structure is proposed based on a subdomain parameterized level set method (PLSM) under local volume constraints (LVC). In this method, a subdomain level set function is defined, parameterized and updated on each subdomain independently making the proposed approach much faster and more cost-effective. Additionally, the microstructures on arbitrary two adjacent subdomains can be connected perfectly without any additional constraint. Furthermore, the local volume constraint for each subdomain is applied by virtue of the augmented Lagrange multiplier method. Finally, several numerical examples are given to verify the correctness and effectiveness of the proposed approach in designing the functionally graded cellular structure. From the optimized results, it is also found that the number of local volume constraints has little influence on the convergence speed of the developed approach.

Improved Memory-Efficient Subdomain Level Discontinuous Galerkin Time Domain Method for Periodic/Quasi-Periodic Structures

An improved memory-efficient subdomain level discontinuous Galerkin time domain (SL-DGTD) method, which utilizes the repetition of the geometries, has been proposed to model the periodic and aperiodic structures with square uniform lattice. Significant reduction of memory consumption has been achieved comparing to the conventional SL-DGTD approach. Based on the previous pioneering work of memory-efficient SL-DGTD method, two improvements have been implemented to extend the scope of application. First, multiple kinds of cells, which can be placed arbitrarily on a square lattice, are allowed after a generalized interface recognition scheme. Therefore, besides the large finite periodic arrays successfully solved by previous memory-efficient SL-DGTD method, the aperiodic and/or the quasi-periodic array can be simulated with much less memory as well. Second, certain part of a cell can be cut out to form a new cell. In other words, the cells can be embedded hierarchically, and this is crucial for an efficient modeling of multiscale structures. The effectiveness and efficiency of these improvements has been validated via modeling both proof-of-idea cases and patch antenna arrays with satisfactory reduction in memory consumption.

Age Differences in the Subcomponents of Executive Functioning.

Across the life span, deficits in executive functioning (EF) are associated with poor behavioral control and failure to achieve goals. Though EF is often discussed as one broad construct, a prominent model of EF suggests that it is composed of three subdomains: inhibition, set shifting, and updating. These subdomains are seen in both younger (YA) and older adults (OA), with performance deficits across subdomains in OA. Therefore, our goal was to investigate whether subdomains of EF might be differentially affected by age, and how these differences may relate to broader global age differences in EF. To assess these age differences, we conducted a meta-analysis at multiple levels, including task level, subdomain level, and of global EF. Based on previous work, we hypothesized that there would be overall differences in EF in OA. Using 1,268 effect sizes from 401 articles, we found overall differences in EF with age. Results suggested that differences in performance are not uniform, such that variability in age effects emerged at the task level, and updating was not as affected by age as other subdomains. These findings advance our understanding of age differences in EF, and stand to inform early detection of EF decline.

What do community paramedics assess? An environmental scan and content analysis of patient assessment in community paramedicine

Patient assessment is a fundamental feature of community paramedicine, but the absence of a recognized standard for assessment practices contributes to uncertainty about what drives care planning and treatment decisions. Our objective was to summarize the content of assessment instruments and describe the state of current practice in community paramedicine home visit programs. We performed an environmental scan of all community paramedicine programs in Ontario, Canada, and used content analysis to describe current assessment practices in home visit programs. The International Classification on Functioning, Disability, and Health (ICF) was used to categorize and compare assessments. Each item within each assessment form was classified according to the ICF taxonomy. A total of 43 of 52 paramedic services in Ontario, Canada, participated in the environmental scan with 24 being eligible for further investigation through content analysis of intake assessment forms. Among the 24 services, 16 met inclusion criteria for content analysis. Assessment forms contained between 13 and 252 assessment items (median 116.5, IQR 134.5). Most assessments included some content from each of the domains outlined in the ICF. At the subdomain level, only assessment of impairments of the functions of the cardiovascular, hematological, immunological, and respiratory systems appeared in all assessments. Although community paramedicine home visit programs may differ in design and aim, all complete multi-domain assessments as part of patient intake. If community paramedicine home visit programs share similar characteristics but assess patients differently, it is difficult to expect that the resulting referrals, care planning, treatments, or interventions will be similar.

A novel subdomain level set method for structural topology optimization and its application in graded cellular structure design

A novel subdomain structural topology optimization method is proposed for the minimum compliance problem based on the level sets with the parameterization of radial basis function (RBF). In this method, the level set function evolves on each subdomain separately and independently according to the requirements of objective functions and additional constraints. This makes the parameterization in the proposed subdomain method much faster and more cost-effective than that in the classical global method, as well as the evolution of the level set function since it can be achieved on each subdomain in parallel. In addition, the microstructures on arbitrary two adjacent subdomains can be connected perfectly, without any mismatch around the interfaces of the microstructures. Several typical examples are conducted to verify the correctness and effectiveness of the developed subdomain method. The effects of some factors on the optimized results are also investigated in detail, such as the RBF types, the connectivity types of microstructures, and the size of subdomain division. Without scale separation assumption, several layered graded cellular structures are successfully designed by employing the proposed method under the condition of corresponding repetition constraints. To improve the computational efficiency, a multi-node extended multiscale finite element method (EMsFEM) is used to solve the structural static equilibrium equation for the three-dimensional layered structure optimization problems. Furthermore, a MATLAB code is also provided in the Appendix for readers to reproduce the results of the two-dimensional problems in this work.

Mixed Total Field/Scattered Field-Based Discontinuous Galerkin Frequency-Domain Method for Subsurface Sensing

To model the responses of electromagnetic surveys for geophysical subsurface sensing, a mixed total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method is proposed in this paper. The proposed TF/SF DGFD method is implemented at a subdomain level based on the domain decomposition technique. Different subdomains can employ either the TF DGFD framework or the SF DGFD framework, which are then coupled through the Riemann transmission condition. To balance the computation efficiency and accuracy for practical applications, the proposed method prefers to using the SF DGFD framework for subdomains with sources and using the TF DGFD framework for the remaining subdomains. At the interfaces between total field and scattered field subdomains, the Riemann transmission condition is slightly modified by incorporating the background fields due to the physically imposed sources in the background media. In this way, the proposed method only requires surface integrals of the background fields as extra overhead instead of elementwise integration of the scattering objects for the purely scattered field-based method, which can improve the computational efficiency. Also, it is more accurate than the purely TF DGFD method given the same mesh. Numerical examples are studied to examine the performance of the proposed method, which is proven to have better accuracy than the TF DGFD method. The TF/SF DGFD method will facilitate modeling of electromagnetic surveys under complicated geophysical environments for subsurface sensing.

P085: What do community paramedics assess? An environmental scan and content analysis of patient assessment in community paramedicine

Introduction: Patient assessment is a fundamental feature of non-emergency community paramedicine (CP) home visit programs. In the absence of a recognized standard for CP assessment, current assessment practices in CP programs are unknown. Without knowing what community paramedics are assessing, it is difficult to ascertain what should be included in patient care plans, whether interventions are beneficial, or whether paramedics are meeting program objectives. Our objective was to summarize the content of assessment instruments used in CP programs in order to describe the state of current practice. Methods: We performed an environmental scan of all CP programs in Ontario, Canada, and employed content analysis to describe current assessment practices in CP home visit programs. The International Classification on Functioning, Disability, and Health (ICF) was used to categorize and compare assessments. Each item within each assessment form was classified according to the ICF taxonomy. Findings were compared at the domain and sub-domain of the ICF. Results: Of 54 paramedic services in Ontario, 43 responded to our request for information. Of 24 services with CP home visit programs, 18 provided their intake assessment forms for content analysis. Assessment forms contained between 13 and 252 assessment items (median 116.5, IQR 134.5). Overall, most assessments included some content from each of the domains outlined in the ICF, including: Impairments of Body Functions, Impairments of Body Structures, Activity Limitation and Participation, and Environmental Factors. At the sub-domain level, only assessment of Impairments of the Functions of the Cardiovascular, Haematological, Immunological and Respiratory systems appeared in all assessments. Few CP home visit program assessments covered most ICF sub-domain categories and many items classified to specific categories were included in only a few assessments. Conclusion: CP home visit programs complete multi-domain assessments as part of patient intake. The content of CP assessments varied across Ontario, which suggests that care planning and resources may not be consistent. Current work on practice guidelines and paramedic training can build from descriptions of assessment practices to improve quality of care and patient safety. By identifying what community paramedics assess, evaluation of the quality of CP home visit programs and their ability to meet program objectives can be improved and benchmarks in patient care can be established.