Effect of interfacial oxidation occurring during the duplex process combining surface nanocrystallisation and co-rolling

International audienceThis paper presents an investigation of the interface quality of nanocristallised 316L stainless steel multilayer structures. They were produced by a duplex process, combining the Surface Mechanical Attrition Treatment (SMAT) and the co-rolling process at two different annealing temperatures (550°C and 650°C). Oxide layers were observed at the interfaces between the sheets and their morphology was characterised by optical microscopy. Their chemical composition was determined by Energy Dispersive X-ray spectrometry. The microstructure near the interfaces was analysed by Transmission Electron Microscopy (TEM). In the laminate co-rolled at 550°C, the presence of ultrafine grains was demonstrated. Additional tensile tests have shown an influence of the annealing temperature on the yield strength, as well as on the resistance of the interfaces of the co-rolled multilayer structures

A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI

© 2020, Springer Nature Switzerland AG. Prostate cancer is the second most commonly occurring cancer in men. Diagnosis through Magnetic Resonance Imaging (MRI) is limited, yet current practice holds a relatively low specificity. This paper extends a previous SPIE ProstateX challenge study in three ways (1) to include healthy tissue analysis, creating a solution suitable for clinical practice, which has been requested and validated by collaborating clinicians; (2) by using a voting ensemble method to assist prostate cancer diagnosis through a supervised SVM approach; and (3) using the unsupervised GTM to provide interpretability to understand the supervised SVM classification results. Pairwise classifiers of clinically significant lesion, non-significant lesion, and healthy tissue, were developed. Results showed that when combining multiparametric MRI and patient level metadata, classification of significant lesions against healthy tissue attained an AUC of 0.869 (10-fold cross-validation)

Automatic relevance source determination in human brain tumors using Bayesian NMF.

The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current study we estimate the number of relevant sources for a set of discrimination problems involving brain tumors and normal brain. For this, we propose to start by calculating a high number of sources using Bayesian NMF and automatically discarding the irrelevant ones during the iterative process of matrices decomposition, hence obtaining a reduced range of interpretable solutions. The real data used in this study come from a widely tested human brain tumor database. Simulated data that resembled the real data was also generated to validate the hypothesis against ground truth. The results obtained suggest that the proposed approach is able to provide a small range of meaningful solutions to the problem of source extraction in human brain tumors

Impact of co-morbid burden on mortality in patients with coronary heart disease, heart failure, and cerebrovascular accident: a systematic review and meta-analysis.

Aims: We sought to investigate the prognostic impact of co-morbid burden as defined by the Charlson Co-morbidity Index (CCI) in patients with a range of prevalent cardiovascular diseases. Methods and results: We searched MEDLINE and EMBASE to identify studies that evaluated the impact of CCI on mortality in patients with cardiovascular disease. A random-effects meta-analysis was undertaken to evaluate the impact of CCI on mortality in patients with coronary heart disease (CHD), heart failure (HF), and cerebrovascular accident (CVA). A total of 11 studies of acute coronary syndrome (ACS), 2 stable coronary disease, 5 percutaneous coronary intervention (PCI), 13 HF, and 4 CVA met the inclusion criteria. An increase in CCI score per point was significantly associated with a greater risk of mortality in patients with ACS [pooled relative risk ratio (RR) 1.33; 95% CI 1.15-1.54], PCI (RR 1.21; 95% CI 1.12-1.31), stable coronary artery disease (RR 1.38; 95% CI 1.29-1.48), and HF (RR 1.21; 95% CI 1.13-1.29), but not CVA. A CCI score of >2 significantly increased the risk of mortality in ACS (RR 2.52; 95% CI 1.58-4.04), PCI (RR 3.36; 95% CI 2.14-5.29), HF (RR 1.76; 95% CI 1.65-1.87), and CVA (RR 3.80; 95% CI 1.20-12.01). Conclusion: Increasing co-morbid burden as defined by CCI is associated with a significant increase in risk of mortality in patients with underlying CHD, HF, and CVA. CCI provides a simple way of predicting adverse outcomes in patients with cardiovascular disease and should be incorporated into decision-making processes when counselling patients

Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy.

Glioblastomas are one the most aggressive brain tumors. Their usual bad prognosis is due to the heterogeneity of their response to treatment and the lack of early and robust biomarkers to decide whether the tumor is responding to therapy. In this work, we propose the use of a semi-supervised methodology for source extraction to identify the sources representing tumor response to therapy, untreated/unresponsive tumor, and normal brain; and create nosological images of the response to therapy based on those sources. Fourteen mice were used to calculate the sources, and an independent test set of eight mice was used to further evaluate the proposed approach. The preliminary results obtained indicate that was possible to discriminate response and untreated/unresponsive areas of the tumor, and that the color-coded images allowed convenient tracking of response, especially throughout the course of therapy

Externally validated models for first diagnosis and risk of progression of knee osteoarthritis.

ObjectiveWe develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST).Materials and methodsThe diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively.ResultsThe classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721-0.774) and 0.670 (0.631-0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325-0.7439) and in external validation 0.72 (0.7190-0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data.DiscussionThe models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years.ConclusionModelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients

Microstructure and mechanical properties of an ODS RAF steel fabricated by hot extrusion or hot isostatic pressing

Ingots of an oxide dispersion strengthened reduced activation ferritic steel with the Fe-14Cr-2W-0.3Ti-0.3Y(2)O(3) chemical composition (in wt.%) were synthesized by mechanical alloying of elemental powders with 0.3 wt.% Y2O3 particles in a planetary ball mill, in a hydrogen atmosphere. and compacted by either hot extrusion or hot isostatic pressing. The microstructures of the obtained materials were characterized by means of light microscopy, transmission electron microscopy and chemical analyses. The mechanical properties were evaluated by means of Vickers microhardness measurements and tensile tests. It was found that the microstructure of both materials is composed of ferritic grains having a submicron size and containing nanometric Y-Ti-O oxide particles with a mean size of about 10 nm, uniformly distributed in the matrix. The oxide particles in the hot extruded steel were identified as YTiO3 phase. In larger (>10 nm) oxide particles Cr was found next to Ti, Y and O. The steel produced by hot extrusion exhibits much higher tensile strength and hardness at low to moderate temperatures, as compared to the steel fabricated by hot isostatic pressing, which was mainly attributed to smaller pores but also to more severe work hardening in the case of the hot extruded steel. (C) 2011 Elsevier B.V. All rights reserved

Urban Water Demand Prediction for a City that Suffers from Climate Change and Population Growth: Gauteng Province case study

The proper management of municipal water system is essential to sustain cities and support water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and Artificial Neural Network (ANN) optimized with Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Also, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth

A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that 1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; 2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision