Predictive analysis of COVID-19 symptoms in social networks through machine learning

Social media is a great source of data for analyses, since they provide ways for people to share emotions, feelings, ideas, and even symptoms of diseases. By the end of 2019, a global pandemic alert was raised, relative to a virus that had a high contamination rate and could cause respiratory complications. To help identify those who may have the symptoms of this disease or to detect who is already infected, this paper analyzed the performance of eight machine learning algorithms (KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT) in the search and classification of tweets that mention self-report of COVID-19 symptoms. The dataset was labeled using a set of disease symptom keywords provided by the World Health Organization. The tests showed that Random Forest algorithm had the best results, closely followed by BERT and Convolution Neural Network, although traditional machine learning algorithms also have can also provide good results. This work could also aid in the selection of algorithms in the identification of diseases symptoms in social media content.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/AI/0088/2020info:eu-repo/semantics/publishedVersio

LIÇÕES DE MORAL E CIVISMO NO ENSINO DE HISTÓRIA: A FORMAÇÃO DO CIDADÃO IDEAL NA VISÃO DO PROFESSOR MELLO E SOUZA

Este artigo reflete sobre as concepções de ensino de História do professor Mello e Souza (1888-1969), sujeito que iniciou a carreira nas primeiras décadas do século XX em meio a ambiciosos projetos de valorização da educação escolar e da cultura letrada. Para analisar suas memórias familiares e trajetórias profissionais, registradas em suportes textuais e imagéticos diversos, dialogamos com referenciais teórico-metodológicos da história cultural e da educação. Ao focalizarmos principalmente a tese elaborada para o concurso do Colégio Pedro II, que discutia a formação do caráter dos alunos a partir dos princípios morais e cívicos encontrados nos conteúdos históricos, problematizamos os limites dos projetos vigentes, que concentraram esforços na difusão de um modelo idealizado de cidadão patriótico

Yin Yang Convolutional Nets: Image Manifold Extraction by the Analysis of Opposites

Computer vision in general presented several advances such as training optimizations, new architectures (pure attention, efficient block, vision language models, generative models, among others). This have improved performance in several tasks such as classification, and others. However, the majority of these models focus on modifications that are taking distance from realistic neuroscientific approaches related to the brain. In this work, we adopt a more bio-inspired approach and present the Yin Yang Convolutional Network, an architecture that extracts visual manifold, its blocks are intended to separate analysis of colors and forms at its initial layers, simulating occipital lobe's operations. Our results shows that our architecture provides State-of-the-Art efficiency among low parameter architectures in the dataset CIFAR-10. Our first model reached 93.32\% test accuracy, 0.8\% more than the older SOTA in this category, while having 150k less parameters (726k in total). Our second model uses 52k parameters, losing only 3.86\% test accuracy. We also performed an analysis on ImageNet, where we reached 66.49\% validation accuracy with 1.6M parameters. We make the code publicly available at: https://github.com/NoSavedDATA/YinYang_CNN.Comment: 12 pages, 5 tables and 6 figure

Assessment of honey bee cells using deep learning

Temporal assessment of honey bee colony strength is required for different applications in many research projects. This task often requires counting the number of cells with brood and food reserves multiple times a year from images taken in the apiary. There are thousands of cells in each frame, which makes manual counting a time-consuming and tedious activity. Thus, the assessment of frames has been frequently been performed in the apiary in an approximate way by using methods such as the Liebefeld. The automation of this process using modern imaging processing techniques represents a major advance. The objective of this work was to develop a software capable of extracting each cell from frame images, classify its content and display the results to the researcher in a simple way. The cells’ contents display a high variation of patterns which added to light variation make their classification by software a challenging endeavor. To address this challenge, we used Deep Neural Networks (DNNs) for image processing. DNNs are known by achieving the state-of-art in many fields of study including image classification, because they can learn features that best describe the content being classified, such as the interior of frame cells. Our DNN model was trained with over 60,000 manually labeled images whose cells were classified into seven classes: egg, larvae, capped larvae, honey, nectar, pollen, and empty. Our contribution is an end-to-end software capable of doing automatic background removal, cell detection, and classification of its content based on an input image. With this software the researcher is able to achieve an average accuracy of 94% over all classes and get better results compared with approximation methods and previous techniques that used handmade features like color and texture.This research was funded through the 2013-2014 BiodivERsA/FACCE-JPJ joint call for research proposals,witht he national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

First record of Leptodactylus caatingae Heyer & Juncá, 2003 (Amphibia, Anura, Leptodactylidae) for the Ceará State, Brazil

Leptodactylus caatingae was described by Heyer & Juncá (2003), based on specimens collected in Bahia. The species is known in the Caatinga and Atlantic Rainforest biomes, in the states of Bahia, Pernambuco, Paraíba, and Espírito Santo. Here, we report the first record of L. caatingae for the state of Ceará

Automatic detection and classification of honey bee comb cells using deep learning

In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).This research was developed in the framework of the project “BeeHope - Honeybee conservation centers in Western Europe: an innovative strategy using sustainable beekeeping to reduce honeybee decline”, funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

Sexualidade no idoso: percepção de profissionais da geriatria e gerontologia

OBJETIVO: Identificar a percepção dos profissionais da saúde acerca da sexualidade em idosos. METODOLOGIA: Estudo quantitativo, observacional, do tipo transversal analítico, em instituição especializada na assistência a terceira idade em Belém-PA. Foi aplicado questionário com 20 profissionais da saúde. Os dados foram submetidos à análise estatística com teste G de aderência. RESULTADOS: Os profissionais, de ambos os sexos, tinham idade média de 41,8 anos (±12,2), formados há aproximadamente 17,1 anos (±12,4). A casuística foi formada por médicos, fisioterapeutas, enfermeiros, assistentes sociais, odontólogos, psicólogo, terapeuta ocupacional, nutricionista e farmacêuticos. Os resultados quanto à percepção dos profissionais sobre a sexualidade evidenciam que 100% relatam saber distinguir sexo de sexualidade, 45% relatou nenhuma formação acadêmica sobre sexualidade em idosos, 75% se sentem razoavelmente preparados para lidar com o tema. Quanto a atuação profissional, 35% ainda acham de nada a pouco importante abordar o tema com os idosos, mas a maioria 75% relata conversar com os idosos sobre o tema, as principais orientações passadas dizem respeito ao uso de preservativos e doenças sexualmente transmissíveis, e as dificuldades mais relatadas dizem respeito à resistência dos idosos ao abordar o tema. Metade dos profissionais reconhece que a precária abordagem interfere muito na qualidade de vida dos mesmos. CONCLUSÃO: Embora os profissionais reconheçam a importância da sexualidade na integralidade do ser, existe carência na formação profissional, resistência dos idosos, e tabus socioculturais que são barreiras para abordagem do tema, consequentemente surge negligência por parte de alguns profissionais podendo interferir na qualidade de vida dos idosos

Aciones de la enfermera en la atención primaria para prevecion del suicídio

Los objetivos, en este estudio, fueron describir las acciones realizadas por el enfermero de la atención básica para prevención del suicidio y discutir el proceso de trabajo orientado a la prevención. Es un estudio del tipo exploratorio-descriptivo, de abordaje cualitativo, realizado con enfermeros de la Estrategia Salud de la Familia. Se utilizó la técnica de análisis de contenido para el tratamiento de los datos. Por medio de los resultados, se reveló que las acciones para prevenir el suicidio en la atención básica necesitan ser insertadas en el proceso de trabajo de enfermeros.Os objetivos, neste estudo, foram descrever as ações realizadas pelo enfermeiro da atenção básica para prevenção do suicídio e discutir o processo de trabalho voltado para prevenção. É estudo do tipo exploratório-descritivo, de abordagem qualitativa, realizado com enfermeiros da Estratégia Saúde da Família. Utilizouse a técnica de análise de conteúdo para o tratamento dos dados. Por meio dos resultados, revelou-se que as ações para prevenção do suicídio na atenção básica necessitam ser inseridas no processo de trabalho de enfermeiros.The objectives in this study were to describe the primary health care nurses’ potential to prevent suicide and discuss the work process focused on prevention. An exploratory and descriptive study with a qualitative approach was undertaken, involving nurses from the Family Health Strategy. The content analysis technique was used to treat the data. The results revealed that the actions to prevent suicide in primary health care need to be included in the nurses’ work process