Adaptive EAGLE dynamic solution adaptation and grid quality enhancement

In the effort described here, the elliptic grid generation procedure in the EAGLE grid code was separated from the main code into a subroutine, and a new subroutine which evaluates several grid quality measures at each grid point was added. The elliptic grid routine can now be called, either by a computational fluid dynamics (CFD) code to generate a new adaptive grid based on flow variables and quality measures through multiple adaptation, or by the EAGLE main code to generate a grid based on quality measure variables through static adaptation. Arrays of flow variables can be read into the EAGLE grid code for use in static adaptation as well. These major changes in the EAGLE adaptive grid system make it easier to convert any CFD code that operates on a block-structured grid (or single-block grid) into a multiple adaptive code

Computational homogenization for multiscale crack modeling: implementational and computational aspects

This is the peer reviewed version of the following article: [Nguyen, V. P., Lloberas-Valls, O., Stroeven, M. and Sluys, L. J. (2012), Computational homogenization for multiscale crack modeling. Implementational and computational aspects. Int. J. Numer. Meth. Engng, 89: 192–226. doi:10.1002/nme.3237], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/nme.3237/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingA computational homogenization procedure for cohesive and adhesive crack modeling of materials with a heterogeneous microstructure has been recently presented in Computer Methods in Applied Mechanics and Engineering (2010, DOI:10.1016/j.cma.2010.10.013). The macroscopic material properties of the cohesive cracks are obtained from the inelastic deformation manifested in a localization band (modeled with a continuum damage theory) at the microscopic scale. The macroscopic behavior of the adhesive crack is derived from the response of a microscale sample representing the microstructure inside the adhesive crack. In this manuscript, we extend the theory presented in Computer Methods in Applied Mechanics and Engineering (2010, DOI:10.1016/j.cma.2010.10.013) with implementation details, solutions for cyclic loading, crack propagation, numerical analysis of the convergence characteristics of the multiscale method, and treatment of macroscopic snapback in a multiscale simulation. Numerical examples including crack growth simulations with extended finite elements are given to demonstrate the performance of the methodPeer ReviewedPostprint (author's final draft

Homogenization-based multiscale crack modelling: from micro-diffusive damage to macro-cracks

The existence of a representative volume element (RVE) for a class of quasi-brittle materials having a random heterogeneous microstructure in tensile, shear and mixed mode loading is demonstrated by deriving traction–separation relations, which are objective with respect to RVE size. A computational homogenization based multiscale crack modelling framework, implemented in an FE2 setting, for quasi-brittle solids with complex random microstructure is presented. The objectivity of the macroscopic response to the micro-sample size is shown by numerical simulations. Therefore, a homogenization scheme, which is objective with respect to macroscopic discretization and microscopic sample size, is devised. Numerical examples including a comparison with direct numerical simulation are given to demonstrate the performance of the proposed method.Peer ReviewedPostprint (author's final draft

On the existence of representative volumes for softening quasi-brittle materials: a failure zone averaging scheme

The concept of the representative volume element (RVE) for softening materials is revised in this contribution. It is demonstrated by means of numerical simulations that there exists a sample which is statistically representative for quasi-brittle materials with random microstructure like concrete. This finding is an important ingredient for homogenization-based multiscale modelling of softening materials.Peer ReviewedPostprint (author's final draft

Expression, Purification, and Biophysical Characterization of a Secreted Anthrax Decoy Fusion Protein in Nicotiana benthamiana.

Anthrax toxin receptor-mediated drug development for blocking anthrax toxin action has received much attention in recent decades. In this study, we produced a secreted anthrax decoy fusion protein comprised of a portion of the human capillary morphogenesis gene-2 (CMG2) protein fused via a linker to the fragment crystallizable (Fc) domain of human immunoglobulin G1 in Nicotiana benthamiana plants using a transient expression system. Using the Cauliflower Mosaic Virus (CaMV) 35S promoter and co-expression with the p19 gene silencing suppressor, we were able to achieve a high level of recombinant CMG2-Fc-Apo (rCMG2-Fc-Apo) protein accumulation. Production kinetics were observed up to eight days post-infiltration, and maximum production of 826 mg/kg fresh leaf weight was observed on day six. Protein A affinity chromatography purification of the rCMG2-Fc-Apo protein from whole leaf extract and apoplast wash fluid showed the homodimeric form under non-reducing gel electrophoresis and mass spectrometry analysis confirmed the molecular integrity of the secreted protein. The N-glycosylation pattern of purified rCMG2-Fc-Apo protein was analysed; the major portion of N-glycans consists of complex type structures in both protein samples. The most abundant (>50%) N-glycan structure was GlcNAc₂(Xyl)Man₃(Fuc)GlcNAc₂ in rCMG2-Fc-Apo recovered from whole leaf extract and apoplast wash fluid. High mannose N-glycan structures were not detected in the apoplast wash fluid preparation, which confirmed the protein secretion. Altogether, these findings demonstrate that high-level production of rCMG2-Fc-Apo can be achieved by transient production in Nicotiana benthamiana plants with apoplast targeting

A collaborative care pathway for patients with suspected angle closure glaucoma spectrum disease

Background: Currently, no specific collaborative care pathway exists that distinguishes open angle glaucoma from narrow angle or angle closure disease. This study evaluates a newly developed referral and collaborative care pathway specifically for patients with angle closure spectrum disease. Methods: The medical records of consecutive patients referred to the Centre for Eye Health for glaucoma assessment were examined, six months before (Pre Suite) and after (Post Suite) the introduction of a novel referral pathway for anterior chamber angle assessment (Angle Suite). Patient demographic and clinical data, the referral letter and practitioner characteristics were extracted. Results: Angle Suite (n = 77) patients had an appointment much sooner compared to Pre (n = 383) and Post Suite (n = 425) patients (p < 0.0001). Following the introduction of Angle Suites, there was a reduction of incidental angle closure disease found in routine, non-angle closure glaucoma assessment. Onward referral was required by 36.4 per cent of patients referred for suspected angle closure disease, while the rest could be discharged back into the community (13.0 per cent) or reviewed at the Centre for Eye Health (50.6 per cent). Multinomial logistic regression found that the presence of an angle description in the referral letter improved the true positive rate for angle closure disease (p < 0.0001). Conclusions: The clinical pathway may reduce the number of incidental angle closure patients and improved the timeliness of appropriate clinical care delivered to a subset of patients who may benefit from prompt medical attention. This pathway provides an opportunity for appropriately staffed and equipped collaborative care clinics to reduce the burden on tertiary level ophthalmic facilities

A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection

Crowdsourcing step-by-step information extraction to enhance existing how-to videos

Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77% precision and 81% recall