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Lignin is a natural polymer that has antioxidant properties. However, a supplied lignin's physical, chemical, and rheological behaviour will differ from the source. Thus, the objective of this work was to investigate the mechanical performance of asphalt mixtures produced with asphalt binders modified by lignins at the contents of 0, 3, 6, and 9%, from pinus and eucalyptus sawdust. The mixtures were evaluated by performing the Splitting Tensile Strength Test, Lottman, Resilient Modulus, and Dynamic Modulus. The results pointed to lignin, both tested, as a good natural polymer due to the increase in resilience of the asphalt mixtures and reduction of susceptibility to moisture for the proposed contents. Therefore, it is suitable for field use. Lignin can be identified as a residual modifier that can provide longer service life to asphalt coatings, presenting itself as an alternative modifier to the synthetic elastomeric polymers. The addition of 3% pinus lignin presented the best mechanical performance, followed by the addition of 9% eucalyptus lignin. [ABSTRACT FROM AUTHOR]

Modification of asphalt binders incorporating polyethylene terephthalate (PET) residues can improve their physical and rheological characteristics while providing secondary environmental benefits. This study evaluated the result of the addition of Micronized PET (MPET) in the contents of 2 and 3% in Petroleum Asphalt Cement with 50/70 penetration (PAC 50/70). The binder properties were evaluated through penetration tests, softening point, rotational viscosity, ductility, infrared spectroscopy, performance grade, and multiple stress creep recovery. The results showed that this modifier provided an increase in the softening point and viscosity, a decrease in penetration, an improvement in permanent deformation and fatigue life. The contents used in this research did not show a significant increase in mixing temperatures. The modifier showed the antioxidant property. Overall, 3% of MPET had the best results in the tests performed. It was concluded that the use of MPET as an asphalt binder modifier is an effective method to improve the properties of the asphalt binder while addressing an environmental concern and preserving natural resources. [ABSTRACT FROM AUTHOR]

Considering the correct disposal of the significant volume of polyethylene terephthalate (PET) waste generated worldwide, interlocking concrete block pavement can be an alternative. Thus, in this study, the performance of interlocking blocks produced with partial replacement of fine aggregate by micronized PET in the percentages of 2.5, 5.0, 7.5, and 10.0% was evaluated. The results indicated that the values of compressive strength, modulus of elasticity, and flexural strength decreased with the increase in PET percentage. The blocks exhibited good abrasion resistance and resistance to sulfate attack. Therefore, the results indicate the promising application of PET for paving blocks. [ABSTRACT FROM AUTHOR]

Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones. • Creating medical imaging data sets requires extensive annotations by radiologists. • Natural language processing allows automated extraction of image labels from reports. • Recently, neural approaches prevail over symbolic and statistical approaches. • All public image data sets generated with this technique consist of chest X-rays. • Public data containing images and reports are needed for proper model benchmarking. [ABSTRACT FROM AUTHOR]

Several systemic diseases affect the retinal blood vessels, and thus, their assessment allows an accurate clinical diagnosis. This assessment entails the estimation of the arteriolar-to-venular ratio (AVR), a predictive biomarker of cerebral atrophy and cardiovascular events in adults. In this context, different automatic and semiautomatic image-based approaches for artery/vein (A/V) classification and AVR estimation have been proposed in the literature, to the point of having become a hot research topic in the last decades. Most of these approaches use a wide variety of image properties, often redundant and/or irrelevant, requiring a training process that limits their generalization ability when applied to other datasets. This paper presents a new automatic method for A/V classification that just uses the local contrast between blood vessels and their surrounding background, computes a graph that represents the vascular structure, and applies a multilevel thresholding to obtain a preliminary classification. Next, a novel graph propagation approach was developed to obtain the final A/V classification and to compute the AVR. Our approach has been tested on two public datasets (INSPIRE and DRIVE), obtaining high classification accuracy rates, especially in the main vessels, and AVR ratios very similar to those provided by human experts. Therefore, our fully automatic method provides the reliable results without any training step, which makes it suitable for use with different retinal image datasets and as part of any clinical routine. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches. [ABSTRACT FROM AUTHOR]

Background: Changes in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task. Metholodogy: A method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection. Results and conclusions: The performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment. [ABSTRACT FROM AUTHOR]

Nasal diphthongs in unstressed final syllable are subject to reduction. A word like "jovem" can be pronounced as [...], [...], [...] or [...]. Based on variationist sociolinguistics, we are analyzing the monophthongization process of final unstressed nasal diphthongs in the city of Maceió / AL. To that aim, spontaneous speech data from 36 participants were analyzed stratified according to gender, age and education. All data were subjected to acoustic analysis. For statistical analysis, we used multilevel regression model, with individuals as more aggregate level. We conclude that there is interaction between social variables such as gender, age and education. Among men, the monophthongization is directly proportional to age; among women, the middle age group performs more the process. In this latter age range, education is not significant. In the other age groups, the less educated, the process is more observed. Regarding linguistic variables, it is concluded that the monophthongization is directly proportional to the size of the word and that the process is more frequent in the diphthong [...]. The process is inhibited in the final statements. [ABSTRACT FROM AUTHOR]

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]

Purpose : This study aims to investigate retinal and choroidal vascular reactivity to carbogen in central serous chorioretinopathy (CSC) patients. Methods: An experimental pilot study including 68 eyes from 20 CSC patients and 14 age and sex-matched controls was performed. The participants inhaled carbogen (5% CO2 + 95% O2) for 2 min through a high-concentration disposable mask. A 30° disc-centered fundus imaging using infra-red (IR) and macular spectral domain optical coherence tomography (SD-OCT) using the enhanced depth imaging (EDI) technique was performed, both at baseline and after a 2-min gas exposure. A parametric model fitting-based approach for automatic retinal blood vessel caliber estimation was used to assess the mean variation in both arterial and venous vasculature. Choroidal thickness was measured in two different ways: the subfoveal choroidal thickness (SFCT) was calculated using a manual caliper and the mean central choroidal thickness (MCCT) was assessed using an automatic software. Results: No significant differences were detected in baseline hemodynamic parameters between both groups. A significant positive correlation was found between the participants' age and arterial diameter variation (p < 0.001, r = 0.447), meaning that younger participants presented a more vasoconstrictive response (negative variation) than older ones. No significant differences were detected in the vasoreactive response between CSC and controls for both arterial and venous vessels (p = 0.63 and p = 0.85, respectively). Although the vascular reactivity was not related to the activity of CSC, it was related to the time of disease, for both the arterial (p = 0.02, r = 0.381) and venous (p = 0.001, r = 0.530) beds. SFCT and MCCT were highly correlated (r = 0.830, p < 0.001). Both SFCT and MCCT significantly increased in CSC patients (p < 0.001 and p < 0.001) but not in controls (p = 0.059 and 0.247). A significant negative correlation between CSC patients' age and MCCT variation (r = − 0.340, p = 0.049) was detected. In CSC patients, the choroidal thickness variation was not related to the activity state, time of disease, or previous photodynamic treatment. Conclusion: Vasoreactivity to carbogen was similar in the retinal vessels but significantly higher in the choroidal vessels of CSC patients when compared to controls, strengthening the hypothesis of a choroidal regulation dysfunction in this pathology. [ABSTRACT FROM AUTHOR]

In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population. [ABSTRACT FROM AUTHOR]

This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task. [ABSTRACT FROM AUTHOR]

One of the by-products of the oil industry that is disposed of in nature as waste is oil well gravel. When improperly disposed of, it pollutes the soil, deteriorates the urban landscape, and constitutes an environmental liability if it is destined for landfills without prior treatment. As a way of reusing gravel from oil wells, this research proposed using gravel as a filler in hot asphalt mixtures. This study evaluated hot asphalt mixtures dosed with 2% filler by the Superpave methodology. The 2% filler was partially replaced by the oil well gravel (1% gravel and 1% lime) and totally (2% gravel), compared to the use of 2% lime as a commonly used filler in traditional blends. Rheological tests such as rotational viscosity, performance grade (PG), creep and recovery under multiple stresses (MSCR), and linear sweep amplitude of strain (LAS) were carried out in order to investigate the effects on asphalt mastic when incorporating oil well gravel in the contents proposed. The asphalt mixtures were evaluated through mechanical tests: tensile strength, modulus of resilience, dynamic modulus, flow number, and modified Lottman. The rheological evaluation could prove the gain in viscosity and stiffness of the asphalt binder, showing less susceptibility to thermal variation and, consequently, an increase in the resistance to permanent deformation. It was also found that the incorporation of the gravel residue caused a reduction in the resistance to damage and shorter fatigue life to the binder at the highest deformation amplitudes. The use of gravel from oil wells did not cause a significant change in the mechanical performance of the asphalt mixtures at the tested contents, presenting itself as a sustainable option for use mainly in regions with low rainfall, such as the Northeast region of Brazil. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55–0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61–0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients. [ABSTRACT FROM AUTHOR]

This paper describes a new methodology for lane detection in Thin-Layer Chromatography images. An approach based on the continuous wavelet transform is used to enhance the relevant lane information contained in the intensity profile obtained from image data projection. Lane detection proceeds in three phases: the first obtains a set of candidate lanes, which are validated or removed in the second phase; in the third phase, lane limits are calculated, and subtle lanes are recovered. The superior performance of the new solution was confirmed by a comparison with three other methodologies previously described in the literature. [ABSTRACT FROM AUTHOR]

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%. [ABSTRACT FROM AUTHOR]

Chest X-ray scans are frequently requested to detect the presence of abnormalities, due to their low-cost and non-invasive nature. The interpretation of these images can be automated to prioritize more urgent exams through deep learning models, but the presence of image artifacts, e.g. lettering, often generates a harmful bias in the classifiers and an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise, in which an image is either normal or abnormal, using an attention-driven and spatially unsupervised S patial T ransform er N etwork (STERN), that takes advantage of a novel domain-specific loss to better frame the region of interest. Unlike the state of the art, in which this type of networks is usually employed for image alignment, this work proposes a spatial transformer module that is used specifically for attention, as an alternative to the standard object detection models that typically precede the classifier to crop out the region of interest. In sum, the proposed end-to-end architecture dynamically scales and aligns the input images to maximize the classifier's performance, by selecting the thorax with translation and non-isotropic scaling transformations, and thus eliminating artifacts. Additionally, this paper provides an extensive and objective analysis of the selected regions of interest, by proposing a set of mathematical evaluation metrics. The results indicate that the STERN achieves similar results to using YOLO-cropped images, with reduced computational cost and without the need for localization labels. More specifically, the system is able to distinguish abnormal frontal images from the CheXpert dataset, with a mean AUC of 85.67% - a 2.55% improvement vs. the 0.98% improvement achieved by the YOLO-based counterpart in comparison to a standard baseline classifier. At the same time, the STERN approach requires less than 2/3 of the training parameters, while increasing the inference time per batch in less than 2 ms. Code available via GitHub. • The proposed spatial transformer allows the system to focus on the thoracic region. • This built-in attention-driven model reduces the negative impact of image artifacts. • A novel loss function and a finetuning stage improve the initial methodology. • A set of proposed metrics evaluate and compare the thoracic region selection. • The end-to-end system outperforms typical object detection followed by classification. [ABSTRACT FROM AUTHOR]

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]

• Chest X-ray multi-label classification benefits from multi-scale approaches. • The ideal input image size varies with the type and size of radiological findings. • Size-specific batch normalization yields a multi-scale parameter-efficient ensemble. • The model generalizes well, making accurate and robust predictions in external data. • Features of different granularities are leveraged and combined, boosting performance. Background and Objective: Convolutional neural networks are widely used to detect radiological findings in chest radiographs. Standard architectures are optimized for images of relatively small size (for example, 224 × 224 pixels), which suffices for most application domains. However, in medical imaging, larger inputs are often necessary to analyze disease patterns. A single scan can display multiple types of radiological findings varying greatly in size, and most models do not explicitly account for this. For a given network, whose layers have fixed-size receptive fields, smaller input images result in coarser features, which better characterize larger objects in an image. In contrast, larger inputs result in finer grained features, beneficial for the analysis of smaller objects. By compromising to a single resolution, existing frameworks fail to acknowledge that the ideal input size will not necessarily be the same for classifying every pathology of a scan. The goal of our work is to address this shortcoming by proposing a lightweight framework for multi-scale classification of chest radiographs, where finer and coarser features are combined in a parameter-efficient fashion. Methods: We experiment on CheXpert, a large chest X-ray database. A lightweight multi-resolution (224 × 224, 448 × 448 and 896 × 896 pixels) network is developed based on a Densenet-121 model where batch normalization layers are replaced with the proposed size-specific batch normalization. Each input size undergoes batch normalization with dedicated scale and shift parameters, while the remaining parameters are shared across sizes. Additional external validation of the proposed approach is performed on the VinDr-CXR data set. Results: The proposed approach (AUC 83.27 ± 0.17 , 7.1M parameters) outperforms standard single-scale models (AUC 81.76 ± 0.18 , 82.62 ± 0.11 and 82.39 ± 0.13 for input sizes 224 × 224, 448 × 448 and 896 × 896, respectively, 6.9M parameters). It also achieves a performance similar to an ensemble of one individual model per scale (AUC 83.27 ± 0.11 , 20.9M parameters), while relying on significantly fewer parameters. The model leverages features of different granularities, resulting in a more accurate classification of all findings, regardless of their size, highlighting the advantages of this approach. Conclusions: Different chest X-ray findings are better classified at different scales. Our study shows that multi-scale features can be obtained with nearly no additional parameters, boosting performance. [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]