Neurological complications after H1N1 influenza vaccination: magnetic resonance imaging findings

Objective: To report 4 different neurological complications of H1N1 virus vaccination. Method: Four patients (9, 16, 37 and 69 years of age) had neurological symptoms (intracranial hypertension, ataxia, left peripheral facial palsy of abrupt onset, altered mental status, myelitis) starting 4-15 days after H1N1 vaccination. MRI was obtained during the acute period. Results: One patient with high T2 signal in the cerebellum interpreted as acute cerebellitis; one patient showed gyriform hyperintensities on FLAIR with sulcal effacement in the right fronto-parietal region; another, with left facial palsy, showed contrast enhancement within both internal auditory canals was present, however it was more important in the right side; and the last one showed findings compatible with thoracic myelitis. Conclusion: H1N1 vaccination can result in important neurological complications probably secondary to post-vaccination inflammation. MRI detected abnormalities in all patients

SciLander: Mapping the Scientific News Landscape

The COVID-19 pandemic has fueled the spread of misinformation on social media and the Web as a whole. The phenomenon dubbed `infodemic' has taken the challenges of information veracity and trust to new heights by massively introducing seemingly scientific and technical elements into misleading content. Despite the existing body of work on modeling and predicting misinformation, the coverage of very complex scientific topics with inherent uncertainty and an evolving set of findings, such as COVID-19, provides many new challenges that are not easily solved by existing tools. To address these issues, we introduce SciLander, a method for learning representations of news sources reporting on science-based topics. We extract four heterogeneous indicators for the sources; two generic indicators that capture (1) the copying of news stories between sources, and (2) the use of the same terms to mean different things (semantic shift), and two scientific indicators that capture (1) the usage of jargon and (2) the stance towards specific citations. We use these indicators as signals of source agreement, sampling pairs of positive (similar) and negative (dissimilar) samples, and combine them in a unified framework to train unsupervised news source embeddings with a triplet margin loss objective. We evaluate our method on a novel COVID-19 dataset containing nearly 1M news articles from 500 sources spanning a period of 18 months since the beginning of the pandemic in 2020. Our results show that the features learned by our model outperform state-of-the-art baseline methods on the task of news veracity classification. Furthermore, a clustering analysis suggests that the learned representations encode information about the reliability, political leaning, and partisanship bias of these sources

Caracterização das áreas geradoras de sedimentos provenientes dos processos erosivos em vertentes escarpadas

The use of digital elevation model allows calculating a series of topographic variables that can assist in the terrain analysis. This article aims to characterize the contributing areas upstream of reservoirs in the watershed of the river Buritis, using topographic variables calculated from the digital elevation model SRTM. These areas have gully erosion features that can generate large amounts of sediments, threatening the sustainability of reservoirs on the study area. The terrain analysis was based on the spatial distribution of the following topographic variables: slope, plane curvature, profile curvature, altitude above drainage network, normalized altitude, wetness index, two-dimensional topographic factor, mass balance index, terrain ruggedness index and upslope area. The hypsometric analysis was performed and visualized using the bipolar differentiation technique. The terrain analysis was carried out using the modules of hydrology and morphometry of the System for Automated Geoscientific Analyses SAGA. The terrain analysis indicates that the reservoirs which supply hydroelectric plants have a contribution area of 755 km2. The area able to generate the sediments that could achieve the reservoirs reaches 63% of the total area of the watershed. Topographic variables calculated from the SRTM data allowed the characterization of the relief forms and their interaction with local erosion processes.Pages: 1389-139

Sistemas de sensores laboratoriais para a análise do comportamento espectral da vegetação sob a influência de diferentes tipos de solo e cobertura vegetal

At the present work, it was analyzed the influence of different kinds of soil and cover in the spectral behavior of vegetation. This kind of study might be done by the acquisition of data in laboratories, using radiometric techniques to force appropriate conditions, without atmospheric interference, that might damage the spectral response from targets. Among the remote sensing techniques used to explore the spectral properties of vegetation are also the vegetation indexes, NDVI, SAVI and RS. The majority of them use the vegetation reflectances in the red and near infrared bands to characterize the growth and development parameters of vegetation. When studying vegetation with remote sensing techniques it is necessary to know the physiology and reflectance spectrum of the kind of studied plant, considering the three parts of the sun radiation after reaching the earth: the absorbed, the reflected and the transmitted ones. The soil spectrum of reflectance depends on its biological, physical, chemical and mineralogical compositions. Some factors that cause influence on its reflectance are the moisture, texture, color and the amount of iron inside its components. The reflectance factor curves were obtained after doing the reflectance measurements in the Laboratory of Radiometry at the National Institute of Space Research (LARAD INPE), by the use of the spectroradiometer FieldSpec Pro FR.Pages: 9048-905

Sistemas de sensores laboratoriais para a análise do comportamento espectral da vegetação sob a influência de diferentes tipos de solo e cobertura vegetal

At the present work, it was analyzed the influence of different kinds of soil and cover in the spectral behavior of vegetation. This kind of study might be done by the acquisition of data in laboratories, using radiometric techniques to force appropriate conditions, without atmospheric interference, that might damage the spectral response from targets. Among the remote sensing techniques used to explore the spectral properties of vegetation are also the vegetation indexes, NDVI, SAVI and RS. The majority of them use the vegetation reflectances in the red and near infrared bands to characterize the growth and development parameters of vegetation. When studying vegetation with remote sensing techniques it is necessary to know the physiology and reflectance spectrum of the kind of studied plant, considering the three parts of the sun radiation after reaching the earth: the absorbed, the reflected and the transmitted ones. The soil spectrum of reflectance depends on its biological, physical, chemical and mineralogical compositions. Some factors that cause influence on its reflectance are the moisture, texture, color and the amount of iron inside its components. The reflectance factor curves were obtained after doing the reflectance measurements in the Laboratory of Radiometry at the National Institute of Space Research (LARAD INPE), by the use of the spectroradiometer FieldSpec Pro FR.Pages: 9048-905