Search

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data. [ABSTRACT FROM AUTHOR]

Deep Learning has been successful in developing e-learning applications, including engineering question classifiers. However, we are not aware of any classification model to date that recognizes the level of difficulty of questions in the Portuguese language. To overcome this challenge, this paper proposes a data-driven classifier model. A corpus was built with exam questions from the "Exame Nacional do Ensino Médio" in Brazil, whose hit level was validated by millions of students and a difficulty label was assigned to them. Then, a pipeline was designed, in which a Convolutional Neural Network (CNN) received the matrix of the question vectors and predicted the probability of the classes of difficulty, in the output layer. Besides, its performance was measured. As a result, a data-driven classifier was obtained that identifies the difficulty of questions. [ABSTRACT FROM AUTHOR]

Brazil has one of the largest mangrove surfaces worldwide. Due to a wide latitudinal distribution, Brazilian mangroves can be found within a large range of environmental conditions. However, little attention has been given to the description of environmental variables driving the distribution of mangrove species in Brazil. In this study, we present a novel and unprecedented description of environmental conditions for all mangroves along the Brazilian coast focusing on species limits. We apply a descriptive statistics and data-driven approach using Self-Organizing Maps and we combine data from terrestrial and marine environmental geodatabases in a Geographical Information System. We evaluate 25 environmental variables (21 bioclimatic variables, three sea surface temperature derivates, and salinity). The results reveal three groups of correlated variables: (i) air temperature derivates and sea surface temperature derivates; (ii) air temperature, potential evapotranspiration and precipitation derivates; and (iii) precipitation derivates, aridity and salinity. Our results unveil new locations of extreme values of temperature and precipitation. We conclude that Rhizophora harrisonii and Rhizophora racemosa are more limited by precipitation and aridity and that they do not necessarily follow a latitudinal gradient. Our data also reveal that the lowest air temperatures of the coldest month are not necessarily found at the southernmost limits of mangroves in Brazil; instead they are localized at the Mesoregion of Vale do Itajaí. However, the minimum sea surface temperature drops gradually with higher latitudes in the Brazilian southern hemisphere and is probably a better indicator for the decrease of species at the latitudinal limits of mangroves than the air temperature and precipitation. [ABSTRACT FROM AUTHOR]

Many algorithms for surface energy balance (SEB) based on remote sensing (RS) have been advanced to determine evapotranspiration (ET). These algorithms were developed for specific conditions (e.g. , sensors, land use, and crop management) in which functions and empirical parameters within its algorithms concur with those conditions. Therefore, this study aims to develop a SEB-RS algorithm for retrieving ET adjusted to in situ observations. The study was conducted in two experimental fields in Brazil with the crops Jatropha curcas, maize, soybean, and sugarcane. We used multispectral images from the orbital sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) coupled in Landsat 8 satellite and from the terrestrial sensor, Altum, on board of an unmanned aerial vehicle. The proposed algorithm termed as Ground-truthed Surface Energy Balance (GT-SEB) is based on physical formulation of SEB-RS algorithms, where two extra computational processes using in situ ET observations were proposed for originating the new algorithm. The first additional process for optimizing the automatic "anchor" pixels selection and another for algorithm parameters optimization. Thus, both processes aim to reduce the difference between the observed ET and estimated by GT-SEB. Being assessed for both orbital (OLI/TIRS) and suborbital (Altum) sensors, the GT-SEB yielded excellent results (root-mean-square-error, RMSE, ≤ 0.48 mm and modified Kling-Gupta efficiency, KGE, ≥ 0.92). In addition to GT-SEB being an optimized algorithm, it uses a classic parameterization of SEB-RS algorithms, providing efficiency and scalability for other remote sensors, climates, and surfaces. • A new algorithm for mapping evapotranspiration (ET) from remote sensing (RS) is proposed. • ET in situ observed is employed to drive an optimal ET mapping. • The uncertainties in the ET mapping via RS are reduced. • The algorithm is scalable for different kind of sensors, climates and surfaces. [ABSTRACT FROM AUTHOR]

This paper presents four case studies in which forest data catalysed shifts in public policy and corporate activities. Brazil greatly reduced deforestation during the period between 2005 and 2014; Cameroon introduced a structured forest concessions regime; Viet Nam achieved their forest transition; and corporate operations around the world invested in supply chain management to alleviate deforestation concerns. We break the problem-solving required for these achievements into four steps: problem recognition, proposal and choice of solution, putting the solution into effect, and monitoring results. At each of these steps, we consider the relevant forest data. Data helped place issues on policymaker agendas, supported reaching sound decisions and enabled quantitative targets. Policy instruments for implementing change were built around available data and forest monitoring helped evaluate progress. The details of these successes can be an inspiration to those interested in improving collection of data on forests that can effectively support decision-making and better policies. There have been impressive recent improvements to many developing countries' national forest monitoring capabilities. The successful examples of data application presented and evaluated here provide insight into how these new data can be effectively leveraged. • Forest data can underpin problem solving and thus catalyse positive change. • Brazil, Cameroon, and Viet Nam built diverse policy instruments on forest data. • Corporate decision-making draws on data for supply chain management. • Investigation of these four successful cases provides insight into how to leverage forest data effectively. [ABSTRACT FROM AUTHOR]

Objective: The empirical dietary inflammatory pattern (EDIP) assesses the inflammatory potential of diet in the US population. The aim of this study was to assess the applicability of the EDIP to a Brazilian population., Methods: Data from 19- to 75-y-old participants of two editions of the cross-sectional population-based Health Survey of Sao Paulo (HS-SP) were used to validate the EDIP (n = 269; HS-SP 2008), develop an EDIP adapted to a Sao Paulo population, the EDIP-SP (n = 441; HS-SP 2008), and replicate EDIP-SP results in an independent sample (n = 501; HS-SP 2015). Dietary data was assessed through two 24-h recalls and one validated food frequency questionnaire. Plasma C-reactive protein (CRP), and nine other inflammatory biomarkers were determined. EDIP was tested for its association with the 10 inflammatory biomarkers. For development of the EDIP-SP, 21 food groups and their contributions to plasma CRP levels were modeled using a stepwise multiple linear regression adjusted for age and sex., Results: The EDIP was not associated with concentrations of inflammatory biomarkers in a Brazilian population. The components of EDIP-SP were processed meats (β = 0.27; P = 0.082), fruits and vegetables (β = -0.12; P = 0.018), and rice and beans (β = -0.27; P = 0.007). EDIP-SP significantly predicted dietary quality (β = -6.1; P < 0.001) and its inflammatory potential was replicated among men (β = 0.36; P = 0.01), but not among women (β = 0.05; P = 0.82)., Conclusion: EDIP was adapted to the Sao Paulo population. EDIP-SP, composed of high processed meat intake and low intake of fruits and vegetables, and rice and beans, constitutes an important tool to investigate dietary quality based on its inflammatory potential, in Brazilian populations., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world's regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country's response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.