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The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.
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Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives.
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The gold standard to detect SARS-CoV-2 infection considers testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. In parallel, X-Ray and CT scans play an important role in the diagnosis and treatment processes. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are typical characteristics of pneumonia caused by COVID-19. This chapter presents an AI-based system using multiple Transfer Learning models for COVID-19 classification using Chest X-Rays. In our experimental design, all the classifiers demonstrated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnosis, false negatives can be particularly harmful because a false negative can lead to patients being incorrectly diagnosed as healthy. These results suggest that our Deep Learning classifiers can accurately classify X-ray exams as normal or indicative of COVID-19 with high confidence.
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In his most quoted study Imagined Communities, Benedict Anderson argues that the invention of the printing press and the rise of print media contributed to a textual representation of the concept of the nation and nationalism. He states that ‘popular’ print culture was also crucial in its contribution to a global exchange that would have reinforced the idea of an ‘imagined community’.1 Anderson further explains that before the eighteenth century, the concept of nation was extensive, as Latin was the language of a broad, vast, imagined community called ‘Christendom’, but as there were changes in the religious communities, such a concept began to be replaced by French and English as vernacular languages of administrative centralization.2 Thus, print capitalism allied to the book market supported by the improvement of communications and the emergence of new and diverse forms of national languages, originated the creation of clusters of small creole ‘imagined political communities’ that were eager to promote new forms of national and cultural consciousness, aimed at widespread literacy through liens of kinship, ethnicity, fraternity, and power loyalties.3 This chapter posits that Anderson's arguments regarding creole nationalism in the new world, fit the particular case of the emergence of the printing, publishing and book-selling culture among a Euro-creole bourgeoisie from Macao with solid kinship, ethnic, commercial and social connections in Hong Kong, Canton, Shanghai and other littoral spaces in the treaty ports in East Asia, and takes these developments as a necessary point of departure. I argue that they used the widespread nature of print media to empower themselves and other community members with the progressive eighteenth-century Enlightenment ideas on rational scientific knowledge. They embraced atheism and anti-clericalism as important elements of enlightenment, thus promoting scientific culture, constitutional monarchy or republican forms of government, social mobility for ethnic minorities, and religious and intellectual tolerance that to a certain extent challenged the Catholic Church and conservative circles.
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The design thinking methodology is a problem-solving approach that involves empathising with end-users, (re)defining problems, brainstorming solutions creatively, and experimenting with prototypes and testing. It has been widely adopted in education to help students develop critical thinking, creativity, and problem-solving skills in design. On the other hand, text-to-image artificial intelligence is a method used to generate images from natural language descriptors (usually referred to as prompts). Design thinking methodology can teach students to think creatively and critically about real-world problems when applied in the classroom. In the context of design teaching at the University of Saint Joseph, Macao, students use the design thinking methodology to develop innovative proposals for furniture design solutions. Combining design thinking methodologies with text-to-image artificial intelligence can further enhance the learning experience by allowing students to generate visual representations of their ideas during the ideation phase. The authors developed a systematic approach to generate images for ideation on furniture design based on prompting text-to-image (PTI). The analysis related students’ results who applied the design thinking methodology without using AI tools and the results generated using a standard text-to-image programme. By combining both methods, teachers can help students develop critical thinking, creativity, and problem-solving skills, while also allowing them to generate visual representations in a different paradigm and, by so, being able to communicate their ideas with the most appropriate support for them.
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Fish represent the largest group of vertebrates and display the greatest diversity of auditory structures. However, studies addressing how the form and function of the auditory system change during development to enhance perception of the acoustic environment are rather sparse in this taxon compared to other vertebrate groups. An ontogenetic perspective of the auditory system in fishes provides a readily testable framework for understanding structure–function relationships. Additionally, studying ancestral models such as fish can convey valuable comparable information across vertebrates, as early developmental events are often evolutionary conserved. This chapter reviews the literature on the morphological development of the fish auditory system, with particular focus on the inner ear structures that evolve from an otic placode during early embryonic development and then continue to undergo differentiation and maturation in the postembryonic phase. Moreover, the chapter provides a systematic overview of how auditory sensitivity develops during ontogeny. Although most studies indicate a developmental improvement in auditory sensitivity, there is considerably species-specific variation. Lastly, the paucity of information and literature concerning the development of auditory capabilities for social communication in fishes is also discussed. Further investigation on the development of structure and function of the fish auditory system is recommended in order to obtain a deeper understanding of how ontogenetic morphological changes in the auditory pathway relate to modifications in acoustic reception, auditory processing, and the capacity to communicate acoustically.
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Born in Magule, Mozambique (Portuguese East Africa), João dos Santos Albasini was a mulatto, the leading intellectual in the main center, Lourenço Marques (today, Maputo), the editor of O Africano (The African) founded 1908, and O Brado Africano (The African Voice) founded in 1918, and a champion of worker and African rights. Often characterized as a republican and a moderate, Albasini had a basic education and an appetite for ideas. He was an avid reader of republican theory, syndicalism, and anarchism – all influential in Portugal – and was familiar with a range of radical ideas circulating in the city's thriving café culture.
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In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested: (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons. (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.
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The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.
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Fish acoustic signals associated with mating behaviour are typically low-frequency sounds produced by males when in close proximity to females. However, some species make sounds that serve the function and follow the design of advertisement calls, well known in insects, anurans, and birds. Close-range courtship acoustic signals may be used by females in mate assessment as they contain information of male quality such as size and condition. For example, sound-dominant frequency, amplitude, and fatigue resistance may signal body size whereas pulse period (i.e. muscle contraction rate) and calling activity are related with body condition in some species. Some signal features, such as sound pulse number, may carry multiple messages including size and condition. Playback experiments on mate choice of a restricted number of species suggest that females prefer vocal to silent males and may use sound frequency, amplitude, and mainly calling rateCalling ratewhen assessing males. The assessment of males by females becomes more challenging when males engage in choruses or when sounds are otherwise masked by anthropogenic noise but almost nothing is known about how these aspects affect mating decisions and fish reproductive success.