Universidade de Fortaleza

The transport of sand and dust by wind is a potent erosional force, creates sand dunes and ripples, and loads the atmosphere with suspended dust aerosols. This paper presents an extensive review of the physics of wind-blown sand and dust on Earth and Mars. Specifically, we review the physics of aeolian saltation, the formation and development of sand dunes and ripples, the physics of dust aerosol emission, the weather phenomena that trigger dust storms, and the lifting of dust by dust devils and other small-scale vortices. We also discuss the physics of wind-blown sand and dune formation on Venus and Titan.

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

Summary Consumers are increasingly avoiding foods containing synthetic colourants, which lead food industries to replace them by natural pigments, such as carotenoids, betalains, anthocyanins and carminic acid. Betalains are water‐soluble nitrogen‐containing pigments, composed of two structural groups: the red‐violet betacyanins and the yellow‐orange betaxanthins. This review synthesises the published literature on basic chemistry of betalains, their sources and chemical stability. Moreover, several works are mentioned which have demonstrated the potent antioxidant activity of betalains, which has been associated with protection against degenerative diseases.

Google Colaboratory (also known as Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning and free-of-charge access to a robust GPU. This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations. This analysis is performed through the use of Colaboratory for accelerating deep learning for computer vision and other GPU-centric applications. The chosen test-cases are a parallel tree-based combinatorial search and two computer vision applications: object detection/classification and object localization/segmentation. The hardware under the accelerated runtime is compared with a mainstream workstation and a robust Linux server equipped with 20 physical cores. Results show that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources. Thus, this service can be effectively exploited to accelerate not only deep learning but also other classes of GPU-centric applications. For instance, it is faster to train a CNN on Colaboratory's accelerated runtime than using 20 physical cores of a Linux server. The performance of the GPU made available by Colaboratory may be enough for several profiles of researchers and students. However, these free-of-charge hardware resources are far from enough to solve demanding real-world problems and are not scalable. The most significant limitation found is the lack of CPU cores. Finally, several strengths and limitations of this cloud service are discussed, which might be useful for helping potential users.

Microservices are an architectural approach emerging out of service-oriented architecture, emphasizing self-management and lightweightness as the means to improve software agility, scalability, and autonomy. This article examines microservice evolution from the technological and architectural perspectives and discusses key challenges facing future microservice developments.

Flavonoids have aroused much interest in research, since they present a great diversity of biological activities observed in vitro, such as: antioxidant effect, modulation of the enzymatic activity and inhibition of cellular proliferation, exerting beneficial effects on the organism, as well as the use of its therapeutic potential. With wide distribution in the plant kingdom represent a class of phenolic compounds that differ in their chemical structure and particular characteristics. The objective of this review was to describe the relevant aspects of flavonoids, reporting the different known groups, the probable mechanisms by which they act, their pharmacological properties and to gain a better understanding of the reported beneficial health effects of these substances. This systematic review consisted of research using scientific databases such as Scopus, Science Direct, PubMed, SciVerse and SciELO, without time limitation. Some pharmacological properties of some flavonoids and their health benefits have been confirmed by previous studies.

Congenital Zika virus infection can cause microcephaly and severe brain abnormalities (1). Congenital Zika syndrome comprises a spectrum of clinical features (2); however, as is the case with most newly recognized teratogens, the earliest documented clinical presentation is expected to be the most severe. Initial descriptions of the effects of in utero Zika virus infection centered prominently on the finding of congenital microcephaly (3). To assess the possibility of clinical presentations that do not include congenital microcephaly, a retrospective assessment of 13 infants from the Brazilian states of Pernambuco and Ceará with normal head size at birth and laboratory evidence of congenital Zika virus infection was conducted. All infants had brain abnormalities on neuroimaging consistent with congenital Zika syndrome, including decreased brain volume, ventriculomegaly, subcortical calcifications, and cortical malformations. The earliest evaluation occurred on the second day of life. Among all infants, head growth was documented to have decelerated as early as 5 months of age, and 11 infants had microcephaly. These findings provide evidence that among infants with prenatal exposure to Zika virus, the absence of microcephaly at birth does not exclude congenital Zika virus infection or the presence of Zika-related brain and other abnormalities. These findings support the recommendation for comprehensive medical and developmental follow-up of infants exposed to Zika virus prenatally. Early neuroimaging might identify brain abnormalities related to congenital Zika infection even among infants with a normal head circumference (4).

The Internet of Things (IoT) is one of the most promising technologies for the near future. Healthcare and well-being will receive great benefits with the evolution of this technology. This paper presents a review of techniques based on IoT for healthcare and ambient-assisted living, defined as the Internet of Health Things (IoHT), based on the most recent publications and products available in the market from industry for this segment. Also, this paper identifies the technological advances made so far, analyzing the challenges to be overcome and provides an approach of future trends. Through selected works, it is possible to notice that further studies are important to improve current techniques and that novel concept and technologies of IoHT are needed to overcome the identified challenges. The presented results aim to serve as a source of information for healthcare providers, researchers, technology specialists, and the general population to improve the IoHT.

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.

A study on surface water quality in the Acaraú Basin, in the North of the state of Ceará, Brazil was performed. Qualitative dynamics of water flowing to the Acaraú River that supplies water to the irrigation project in the area was evaluated. Multivariate Factor Analysis/Principal Component Analysis was used for evaluation of water quality in order to develop a water quality index (WQI) that reflects soil salinity and sodicity risks and water toxicity to plants. From April/2003 to September/2005 water were sampled from ten sampling sites covering the basin, where physical and chemical parameters that contribute to the WQI were evaluated. The results showed that the use of water for irrigation in the Acaraú basin, according to the proposed WQI, are potentially prone to cause toxicity (crop cycle) and sodicity problem in the long run, if the soil-water-plant is not carefully monitored.

CONTEXT: Reflective practice has been suggested to be an important instrument in improving clinical judgement and developing medical expertise. Empirical evidence supporting this suggestion, however, is absent. This paper reports on an experiment conducted to study the effects of reflective practice on diagnostic accuracy. METHODS: Participants were 42 internal medicine residents in hospitals in 2 states in the northeast of Brazil. They diagnosed 16 clinical cases. The experiment employed a repeated measures design, with 2 independent variables: the complexity of clinical cases (simple or complex), and the reasoning approach induced to diagnose the case (participants were instructed to diagnose each case either through pattern recognition or reflective reasoning). The dependent variable was the accuracy of the diagnosis provided for each case. All participants participated in each of the 2 levels of both independent variables. RESULTS: A main effect of case complexity emerged. There was no statistically significant main effect of reflective practice. However, a significant interaction effect was found between case complexity and mode of processing (F[1,41] = 4.48, P < 0.05), indicating that although reflective practice did not make a difference to accuracy of diagnosis in simple cases, it had a positive effect when diagnosing complex cases. CONCLUSIONS: Reflective practice had a positive effect on diagnosis of complex, unusual cases. Non-analytical reasoning was shown to be as effective as reflective reasoning for diagnosing routine clinical cases. Findings support the idea that reflective practice may particularly improve diagnoses in situations of uncertainty and uniqueness, reducing diagnostic errors.

The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.

Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (~0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

Nanocellulose-based nanocomposite hydrogels are promising materials in different fields of application such as medicine, food, and agriculture.

BACKGROUNDS: The production of free radicals has a role in the regulation of biological function, cellular damage, and the pathogenesis of central nervous system conditions. Epilepsy is a highly prevalent serious brain disorder, and oxidative stress is regarded as a possible mechanism involved in epileptogenesis. Experimental studies suggest that oxidative stress is a contributing factor to the onset and evolution of epilepsy. OBJECTIVE: A review was conducted to investigate the link between oxidative stress and seizures, and oxidative stress and age as risk factors for epilepsy. The role of oxidative stress in seizure induction and propagation is also discussed. RESULTS/CONCLUSIONS: Oxidative stress and mitochondrial dysfunction are involved in neuronal death and seizures. There is evidence that suggests that antioxidant therapy may reduce lesions induced by oxidative free radicals in some animal seizure models. Studies have demonstrated that mitochondrial dysfunction is associated with chronic oxidative stress and may have an essential role in the epileptogenesis process; however, few studies have shown an established link between oxidative stress, seizures, and age.

We understand the integrality in people's care, groups and collectivity having the client as a historical, social and political subject, integrated to his family context, to the environment and the society in which he is inserted. In this scenery the importance of education actions in health is highlighted as an integrating strategy of a collective knowledge that translates in the individual his autonomy and emancipation. Based on this comprehension the study aims to reflect on the principle of the integrality as an axis director of the education actions in health. The education in health as a pedagogical and political process requests the development of a critical and reflexive thinking, allowing to reveal the reality and to propose transforming actions, while historical and social subject able to propose and to give opinions in the decisions of health for his own care, of his family and of the collectivity.

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used medications associated with nephrotoxicity, especially when used chronically. Factors such as advanced age and comorbidities, which in themselves already lead to a decrease in glomerular filtration rate, increase the risk of NSAID-related nephrotoxicity. The main mechanism of NSAID action is cyclooxygenase (COX) enzyme inhibition, interfering on arachidonic acid conversion into E2 prostaglandins E2, prostacyclins and thromboxanes. Within the kidneys, prostaglandins act as vasodilators, increasing renal perfusion. This vasodilatation is a counter regulation of mechanisms, such as the renin-angiotensin-aldosterone system works and that of the sympathetic nervous system, culminating with compensation to ensure adequate flow to the organ. NSAIDs inhibit this mechanism and can lead to acute kidney injury (AKI). High doses of NSAIDs have been implicated as causes of AKI, especially in the elderly. The main form of AKI by NSAIDs is hemodynamically mediated. The second form of NSAID-induced AKI is acute interstitial nephritis, which may manifest as nephrotic proteinuria. Long-term NSAID use can lead to chronic kidney disease (CKD). In patients without renal diseases, young and without comorbidities, NSAIDs are not greatly harmful. However, because of its dose-dependent effect, caution should be exercised in chronic use, since it increases the risk of developing nephrotoxicity.

BACKGROUND: We aimed to assess medical students' empathy and its associations with gender, stage of medical school, quality of life and burnout. METHOD: A cross-sectional, multi-centric (22 medical schools) study that employed online, validated, self-reported questionnaires on empathy (Interpersonal Reactivity Index), quality of life (The World Health Organization Quality of Life Assessment) and burnout (the Maslach Burnout Inventory) in a random sample of medical students. RESULTS: Out of a total of 1,650 randomly selected students, 1,350 (81.8%) completed all of the questionnaires. Female students exhibited higher dispositional empathic concern and experienced more personal distress than their male counterparts (p<0.05; d ≥ 0.5). There were minor differences in the empathic dispositions of students in different stages of their medical training (p<0.05; f<0.25). Female students had slightly lower scores for physical and psychological quality of life than male students (p<0.05; d<0.5). Female students scored higher on emotional exhaustion and lower on depersonalization than male students (p<0.001; d<0.5). Students in their final stage of medical school had slightly higher scores for emotional exhaustion, depersonalization and personal accomplishment (p<0.05; f<0.25). Gender (β = 0.27; p<0.001) and perspective taking (β = 0.30; p<0.001) were significant predictors of empathic concern scores. Depersonalization was associated with lower empathic concern (β = -0.18) and perspective taking (β = -0.14) (p<0.001). Personal accomplishment was associated with higher perspective taking (β = 0.21; p<0.001) and lower personal distress (β = -0.26; p<0.001) scores. CONCLUSIONS: Female students had higher empathic concern and personal distress dispositions. The differences in the empathy scores of students in different stages of medical school were small. Among all of the studied variables, personal accomplishment held the most important association with decreasing personal distress and was also a predicting variable for perspective taking.