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Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.

The future of quantum communication relies on quantum networks composed by observers sharing multipartite quantum states. The certification of multipartite entanglement will be crucial to the usefulness of these networks. In many real situations it is natural to assume that some observers are more trusted than others in the sense that they have more knowledge of their measurement apparatuses. Here we propose a general method to certify all kinds of multipartite entanglement in this asymmetric scenario and experimentally demonstrate it in an optical experiment. Our results, which can be seen as a definition of genuine multipartite quantum steering, give a method to detect entanglement in a scenario in between the standard entanglement and fully device-independent scenarios, and provide a basis for semi-device-independent cryptographic applications in quantum networks. Quantum communications operate with shared multipartite entangled states, and this has to be certified in a setting where not all parties are trusted in the same way. Here the authors propose a method to certify multipartite entanglement in asymmetric scenarios and demonstrate it in an optical experiment.

•Atomic force microscopy to study time since deposition of RBCs in bloodstains.•Different supports influence the structural features of RBCs on bloodstains.•Force spectroscopy as a reliable tool to determine the membrane elasticity of RBCs. Bloodstains can provide important information about a criminal act. These biological traces, when analyzed at murder sites, for example, can determine the dynamics of a criminal offense, the identity of a suspect, and the time at which a crime was committed. Determine the time since deposition (TSD) of these blood traces may be the first clue for the police investigators to estimate the time-lapse of a murder. During a criminal attack, the blood spilled from an injury begins the process of degradation and aging from the moment it leaves the human body and comes into contact with the physical environment. The biophysical properties (morphology and elasticity) of red blood cells (RBCs) undergo several changes when outside the human body, which can be analyzed using microscopic techniques such as atomic force microscopy (AFM). Aiming to apply the AFM/force spectroscopy techniques in the analysis of criminal traces, the present study investigated the TSD for blood smears by analyzing possible changes in the RBCs of a group of voluntary donors. Also, we investigated whether there was any difference in TSD analysis after blood smears deposition onto three different surfaces (glass, metal, or ceramic); and finally, we evaluated force×distance curves obtained from deformation of the membrane surface of RBCs as a function of time. The qualitative results apparently showed that there is no perceptible difference in the structure of RBCs when AFM images were analyzed by simple visual comparison over 28 days (T0–T5). Nevertheless, our quantitative results, measured by AFM, demonstrated the increasing trend of the measurements, such as average height (μm), perimeter (μm), area (μm2) and volume (μm3) of these cells during that period. Additionally, the type of surface of bloodstain deposition should be considered during analyses for the TSD, and the results obtained on glass, metal, or ceramic supports showed significant differences. Therefore, the use of force spectroscopy to obtain force×distance curves for the forensic science approach has been shown to have applicability for the calculation of TSD in the RBCs present in the blood smears. In spite of the promising observations obtained, the use of AFM in crime scenes still requires the expansion and development of more studies for a definitive evaluation of the TSD for blood spots.

Entanglement is one of the most studied properties of quantum mechanics for its application in quantum information protocols. Nevertheless, detecting the presence of entanglement in large multipartite states continues to be a great challenge both from the theoretical and the experimental point of view. Most of the known methods either have computational costs that scale inefficiently with the number of particles or require more information on the state than what is attainable in everyday experiments. We introduce a new technique for entanglement detection that provides several important advantages in these respects. First, it scales efficiently with the number of particles, thus allowing for application to systems composed by up to few tens of particles. Second, it needs only the knowledge of a subset of all possible measurements on the state, therefore being apt for experimental implementation. Moreover, since it is based on the detection of nonlocality, our method is device independent. We report several examples of its implementation for well-known multipartite states, showing that the introduced technique has a promising range of applications.

Natural fibre-reinforced composites have attracted a great deal of attention by the automotive industry mainly due to their sustainable characteristics and low cost. The use of sustainable composites is expected to continuously increase in this area as the cost and weight of vehicles could be partially reduced by replacing glass fibre composites and aluminium with natural fibre composites. Adhesive bonding is the preferred joining method for composites and is increasingly used in the automotive industry. However, the literature on natural fibre reinforced polymer composite adhesive joints is scarce and needs further investigation. The main objective of this study was to investigate experimentally adhesively bonded joints made of natural, synthetic and interlaminar hybrid fibre-reinforced polymer composites. The effect of the number of the interlaminar synthetic layers required in order to match the bonded joint efficiency of a fully synthetic GFRP bonded joint was studied. It was found that the failure load of the hybrid jute/glass adherend joints increased by increasing the number of external synthetic layers (i.e. the failure load of hybrid 3-layer joint increased by 28.6% compared to hybrid 2-layer joint) and reached the pure synthetic adherends joints efficiency due to the optimum compromise between the adherend material property (i.e. stiffness and strength) and a diminished bondline peel stress state.

The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by 82.26 % , 98.93 % , and 65.03 % compared to its respective single model. The HS employing the LSTM improved 92.15 % , 98.69 % , and 32.41 % concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

In subjects with obstructive respiratory diseases the increased work of breathing during exercise can trigger greater recruitment and fatigue of respiratory muscles. Associated with these changes, lower limb muscle dysfunctions, further contribute to exercise limitations. We aimed to assess electrical activity and fatigue of two respiratory and one locomotor muscle during Incremental Shuttle Walking Test (ISWT) in individuals with obstructive respiratory diseases and compare with healthy. This is a case-control study. Seventeen individuals with asthma (asthma group) and fifteen with chronic obstructive pulmonary disease (COPD group) were matched with healthy individuals (asthma and COPD control groups). Surface electromyographic (sEMG) activity of sternocleidomastoid (SCM), scalene (ESC), and rectus femoris (RF) were recorded during ISWT. sEMG activity was analyzed in time and frequency domains at baseline and during the test (33%, 66%, and 100% of ISWT total time) to obtain, respectively, signal amplitude and power spectrum density (EMG median frequency [MF], high- and low-frequency bands, and high/low [H/L] ratio). Asthma group walked a shorter distance than controls (p = 0.0007). sEMG amplitudes of SCM, ESC, and RF of asthma and COPD groups were higher at 33% and 66% of ISWT compared with controls groups (all p<0.05). SCM and ESC of COPD group remained higher until 100% of the test. MF of ESC and RF decreased in asthma group (p = 0.016 and p < 0.0001, respectively) versus controls, whereas MF of SCM (p < 0.0001) decreased in COPD group compared with controls. H/L ratio of RF decreased (p = 0.002) in COPD group versus controls. Reduced performance is accompanied by increased electromyographic activity of SCM and ESC and activation of RF in individuals with obstructive respiratory diseases during ISWT. These are susceptible to be more pronounced respiratory and peripheral muscle fatigue than healthy subjects during exercise.

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

BackgroundRecently, middle meningeal artery (MMA) embolization has emerged as a potentially safe and effective method of treating chronic subdural hematoma (cSDH).ObjectiveTo report a single-center experience with MMA embolization and examines the type of embolic material used, the extent of penetration, and the number of MMA branches embolized.MethodsA retrospective analysis of all patients with MMA embolization from 2018 through 2019 was performed. A failed outcome was defined as either surgical rescue and/or greater than 10 mm of hematoma residual or reaccumulation following embolization.ResultsOf 35 patients, surgery had failed for 9 (26%) and initial conservative treatment had failed for 6 (17%). Of 41 MMA embolizations, including those in six patients with bilateral cSDH who underwent bilateral MMA embolization, 29 (72%) were performed using ethylene vinyl alcohol copolymer (Onyx), 7 (17%) using particles, and 5 (12%) using n-butyl cyanoacrylate. Both the anterior and posterior MMA divisions were embolized in 29 cases (71%); distal penetration of these branches was achieved in 25 embolizations (61%). Twenty-six (63%) cSDHs completely resolved. Complete resolution was seen in 22 of 29 hematomas (76%) in which both anterior and posterior MMA branches were occluded versus 4 of 12 (33%) following single-branch embolization (p=0.014). Embolization of one cSDH (2%) failed.ConclusionMMA embolization of cSDHs appears to be both safe and efficacious. Furthermore, embolization of both the anterior and posterior MMA branches may be associated with increased odds of complete resolution.