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Background: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases. Methods: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a downsampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm. Results: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions. Discussion: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a new methodology for automatic location of the optic disc in retinal images, based on the combination of information taken from the blood vessel network with intensity data. The distribution of vessel orientations around an image point is quantified using the new concept of entropy of vascular directions. The robustness of the method for OD localization is improved by constraining the search for maximal values of entropy to image areas with high intensities. The method was able to obtain a valid location for the optic disc in 1357 out of the 1361 images of the four datasets. [Copyright &y& Elsevier]

Highlights • Diagnosis of pulmonary nodules is fundamental to improve the survival rate of patients. • Accurate segmentation leads to measure a nodule volume or characterize its morphology properly. • Proposing a new method to automatically segment and measure the volume of pulmonary nodules. • Studying on the metaheuristic search based on evolutionary computation. • Results validation on the LIDC-IDRI dataset. Abstract This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative – LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R -value ≥ 92.16% with a significance level of 5%. [ABSTRACT FROM AUTHOR]

• The use of 3 and 5 % SFFA as asphalt binder modifier reduced the maximum PG temperature of the virgin one. • 5% SFFA indicated 40% of RAP as the viable content to the mixture by using the blending chart method. • Recycled asphalt mixtures showed a better mechanical performance and strength gain with SFFA. • The addition of RAP resulted in a higher circularity index for the recycled mixtures. • Production and recovery costs of recycled asphalt mixtures are lower than traditional mixtures. The growing demand for advantages and energy and the increase in the economy justify the reduction of the circular environmental impact, which seeks to reduce the circular environmental impact. The use of recycled asphalt pavement (Reclaimed Asphalt Pavement – RAP) in the production of hot asphalt mixes (Hot Asphalt Mixtures – HMA) has been used in paving due to virgin materials. However, the use of high percentages to its RAP damage is susceptible to failure at low temperatures due to fatigue. The addition of rejuvenating agents can restore the properties of the recovered binder. In this sense, research that addresses solutions with industrial parts and by-products is likely to bring solutions that foster a tendency to work from a circular economy perspective, based on the economy and the circulation of waste and by-products between different sectors including engineering of paving. This work evaluated soybean oil sludge fatty acid (SSFA) at contents of 3 and 5% as a rejuvenator for recycled asphalt agents with 40% RAP, analyzing the possibility of technical, economic, and environmental feasibility. The latter, considering the potential for circularity of materials. Asphalts were mixed with mixtures for mechanical analysis through tests of resistance to attraction, susceptibility to moisture, modulus of resilient, modulus, permanent formation, and fatigue. The circularity assessment was performed through the circularity index (MCI), verifying the recycling potential of the RAP, and the net value presented (NPV) was calculated to analyze the costs of recycled asphalt mixtures. In addition to making RAP, it improved the resistance to permanent deformation, and the SSFA showed a rejuvenating effect on recycled asphalt mixtures, improving fatigue performance at the tested contents. The recycled mixtures showed the highest circularity indexes. The incorporation of accruals increased as accrued SSFA costs did not increase. Therefore, recycled asphalt mixtures proved to be more technically, environmentally, and more viable than the conventional asphalt mixture, especially the mixture with 3% SSFA. [ABSTRACT FROM AUTHOR]

Abstract: Background: E-cadherin is a cell–cell adhesion molecule and the dysfunction of which is a common feature of more than 70% of all invasive carcinomas, including gastric cancer. Mechanisms behind the loss of E-cadherin function in gastric carcinomas include mutations and silencing at either the DNA or RNA level. Nevertheless, in a high percentage of gastric carcinoma cases displaying E-cadherin dysfunction, the mechanism responsible for E-cadherin dysregulation is unknown. We have previously demonstrated the existence of a bi-directional cross-talk between E-cadherin and two major N-glycan processing enzymes, N-acetylglucosaminyltransferase-III or -V (GnT-III or GnT-V). Methods: In the present study, we have characterized the functional implications of the N-glycans catalyzed by GnT-III and GnT-V on the regulation of E-cadherin biological functions and in the molecular assembly and stability of adherens-junctions in a gastric cancer model. The results were validated in human gastric carcinoma samples. Results: We demonstrated that GnT-III induced a stabilizing effect on E-cadherin at the cell membrane by inducing a delay in the turnover rate of the protein, contributing for the formation of stable and functional adherens-junctions, and further preventing clathrin-dependent E-cadherin endocytosis. Conversely, GnT-V promotes the destabilization of E-cadherin, leading to its mislocalization and unstable adherens-junctions with impairment of cell–cell adhesion. Conclusions: This supports the role of GnT-III on E-cadherin-mediated tumor suppression, and GnT-V on E-cadherin-mediated tumor invasion. General significance: These results contribute to fill the gap of knowledge of those human carcinoma cases harboring E-cadherin dysfunction, opening new insights into the molecular mechanisms underlying E-cadherin regulation in gastric cancer with potential translational clinical applications. [Copyright &y& Elsevier]

• DR|GRADUATE is a novel deep learning-based approach for diabetic retinopathy (DR) grading • DR|GRADUATE provides an uncertainty and explanation associated with each prediction • state-of-the-art performance was achieved in several DR-labeled datasets • higher uncertainty cases tend to be associated with worse DR grading performance • the explanation map highlights the most relevant regions for the classification Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patient, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided diagnosis (CAD) systems, but their black-box behaviour hinders clinical application. We propose DR|GRADUATE, a novel deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted. We designed DR|GRADUATE taking into account the ordinal nature of the DR grading problem. A novel Gaussian-sampling approach built upon a Multiple Instance Learning framework allow DR|GRADUATE to infer an image grade associated with an explanation map and a prediction uncertainty while being trained only with image-wise labels. DR|GRADUATE was trained on the Kaggle DR detection training set and evaluated across multiple datasets. In DR grading, a quadratic-weighted Cohen's kappa (κ) between 0.71 and 0.84 was achieved in five different datasets. We show that high κ values occur for images with low prediction uncertainty, thus indicating that this uncertainty is a valid measure of the predictions' quality. Further, bad quality images are generally associated with higher uncertainties, showing that images not suitable for diagnosis indeed lead to less trustworthy predictions. Additionally, tests on unfamiliar medical image data types suggest that DR|GRADUATE allows outlier detection. The attention maps generally highlight regions of interest for diagnosis. These results show the great potential of DR|GRADUATE as a second-opinion system in DR severity grading. [ABSTRACT FROM AUTHOR]