Bayesian parameter estimation for the inclusion of uncertainty in progressive damage simulation of composites.

Despite gradual progress over the past decades, the simulation of progressive damage in composite laminates remains a challenging task, in part due to inherent uncertainties of material properties. This paper combines three computational methods - finite element analysis (FEA), machine learning and Markov Chain Monte Carlo - to estimate the probability density of FEA input parameters while accounting for the variation of mechanical properties. First, 15,000 FEA simulations of open-hole tension tests are carried out with randomly varying input parameters by applying continuum damage mechanics material models. This synthetically-generated data is then used to train and validate a neural network consisting of five hidden layers and 32 nodes per layer to develop a highly efficient surrogate model. With this surrogate model and the incorporation of statistical test data from experiments, the application of Markov Chain Monte Carlo algorithms enables Bayesian parameter estimation to learn the probability density of input parameters for the simulation of progressive damage evolution in fibre reinforced composites. This methodology is validated against various open-hole tension test geometries enabling the determination of virtual design allowables.

Multiscale characterization and representation of composite materials during processing.

Given the importance of residual stresses and dimensional changes in composites manufacturing, process simulation has been the focus of many studies in recent years. Consequently, various constitutive models and simulation approaches have been developed and implemented for composites process simulation. In this paper, various constitutive models, ranging from elastic to nonlinear viscoelastic; and simulation approaches ranging from separated flow/solid phases to multiscale integrated phases are presented and their applicability for process simulation is discussed. Attention has been paid to practical aspects of the problem where the complexity of the model coupled with the complexity and size scaling of the structure increases the characterization and simulation costs. Two specific approaches and their application are presented in detail: the pseudo-viscoelastic cure hardening instantaneously linear elastic (CHILE) and linear viscoelastic (VE). It is shown that CHILE can predict the residual stress formation in simple cure cycles such as the one-hold cycle for HEXCEL AS4/8552 where the material does not devitrify during processing. It is also shown that using this simple approach, the cure cycle can be modified to lower the residual stress level and therefore increase the mechanical performance of the composite laminate. For a more complex cure cycle where the material is devitrified during a post-cure, it is shown that a more complex model such as VE is required. This article is part of the themed issue 'Multiscale modelling of the structural integrity of composite materials'.

Clinical and functional significance of α9ß1 integrin expression in breast cancer: a novel cell-surface marker of the basal phenotype that promotes tumour cell invasion.

Integrin α9ß1 is a receptor for ECM proteins, including Tenascin-C and the EDA domain of fibronectin, and has been shown to transduce TGFß signalling. This study has examined the expression pattern of α9ß1 in 141 frozen breast carcinoma samples and related expression to prognostic indices, molecular subtype and patient outcome. Effects of α9ß1 on tumour cell migration and invasion were assessed using blocking antibody and gene transduction approaches. Integrin α9ß1 localized to myoepithelial cells in normal ducts and acini, a pattern maintained in DCIS. A subset (17%) of invasive carcinomas exhibited tumour cell expression of α9ß1, which related significantly to the basal-like phenotype, as defined by either CK5/6 or CK14 expression. Tumour expression of α9ß1 showed a significant association with reduced overall patient survival (p < 0.0001; HR 5.94, 95%CI 3.26-10.82) and with reduced distant-metastasis-free survival (p < 0.0001; HR 6.37, CI 3.51-11.58). A series of breast cancer cell lines was screened for α9ß1 with the highly invasive basal-like GI-101 cell line expressing significant levels. Both migration and invasion of this line were reduced significantly in the presence of α9-blocking antibody and following α9-knockdown with siRNA. Conversely, migratory and invasive behaviour of α9-negative MCF7 cells and α9-low MDA MB468 cells was enhanced significantly by over-expression of α9. Thus, α9ß1 acts as a novel marker of the basal-like breast cancer subtype and expression is associated with reduced survival, while its ability to promote breast cancer cell migration and invasion suggests that it contributes to the aggressive clinical behaviour of this tumour subtype.

Tissue Selection for PD-L1 Testing in Triple Negative Breast Cancer (TNBC).

Atezolizumab in combination with nab-paclitaxel has been introduced for the treatment of locally advanced or recurrent triple negative breast cancer (TNBC). Patient selection relies on the use of immunohistochemistry using a specific monoclonal PD-L1 antibody (clone SP142) in a tightly controlled companion diagnostic test (CDx) with a defined interpretative algorithm. Currently there are no standardized recommendations for selecting the optimal tissue to be tested and there is limited data to support decision making, raising the possibility that tissue selection may bias test results. We compared PD-L1 SP142 assessment in a collection of 73 TNBC cases with matched core biopsies and excision samples. There was good correlation between PD-L1-positive core biopsy and subsequent excision, but we found considerable discrepancy between PD-L1 negative core biopsy and matched excision, with a third of cases found negative on core biopsies converting to positive upon examination of the excision tissue. In view of these findings, we developed a workflow for the clinical testing of TNBC for PD-L1 and implemented it in a central referral laboratory. We present audit data from the clinical PD-L1 testing relating to 2 years of activities, indicating that implementation of this workflow results in positivity rates in our population of TNBC similar to those of IMpassion130 clinical trial. We also developed an online atlas with a precise numerical annotation to aid pathologists in the interpretation of PD-L1 scoring in TNBC.

TBDQ: A Pragmatic Task-Based Method to Data Quality Assessment and Improvement.

Organizations are increasingly accepting data quality (DQ) as a major key to their success. In order to assess and improve DQ, methods have been devised. Many of these methods attempt to raise DQ by directly manipulating low quality data. Such methods operate reactively and are suitable for organizations with highly developed integrated systems. However, there is a lack of a proactive DQ method for businesses with weak IT infrastructure where data quality is largely affected by tasks that are performed by human agents. This study aims to develop and evaluate a new method for structured data, which is simple and practical so that it can easily be applied to real world situations. The new method detects the potentially risky tasks within a process, and adds new improving tasks to counter them. To achieve continuous improvement, an award system is also developed to help with the better selection of the proposed improving tasks. The task-based DQ method (TBDQ) is most appropriate for small and medium organizations, and simplicity in implementation is one of its most prominent features. TBDQ is case studied in an international trade company. The case study shows that TBDQ is effective in selecting optimal activities for DQ improvement in terms of cost and improvement.

A Wavelet-packet-based approach for breast cancer classification.

In this paper, a new approach for non-invasive diagnosis of breast diseases is tested on the region of the breast without undue influence from the background and medically unnecessary parts of the images. We applied Wavelet packet analysis on the two-dimensional histogram matrices of a large number of breast images to generate the filter banks, namely sub-images. Each of 1250 resulting sub-images are used for computation of 32 two-dimensional histogram matrices. Then informative statistical features (e.g. skewness and kurtosis) are extracted from each matrix. The independent features, using 5-fold cross-validation protocol, are considered as the input sets of supervised classification. We observed that the proposed method improves the detection accuracy of Architectural Distortion disease compared to previous works and also is very effective for diagnosis of Spiculated Mass and MISC diseases.

Soft-tissue modeling and image-guided control of steerable needles.

In this paper, we exploit a fuzzy controller on a flexible bevel-tip needle to manipulate the needle's base in order to steer its tip in a preset obstacle-free and target-tracking path. Although the needle tends to follow a curvature path, spinning the needle with an extremely high rotational velocity makes it symmetric with respect to the tissue to follow a straight path. The fuzzy controller determines an appropriate spinning to generate the planned trajectory and, the closed-loop system tries to match the needle body with that trajectory. The swine's brain tissue model, extracted from an in-vitro experimental setup, is a non-homogenous, uncertain and fast-updatable network to model real tissues, needle and their interactions providing the essential visual feedback for the control system. The simulation results illustrate a precise path tracking of the bevel-tip needle based on the fuzzy controller's commands with two degrees of freedom.