A deep learning approach based on graphs to detect plantation lines.

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.

Distribution of cases of congenital heart disease in a hospital in Oeste Paulista / Distribuição dos casos de cardiopatias congênitas em um hospital do Oeste Paulista

The aim of this research was to conduct a comprehensive spatial-temporal analysis of the population affected by congenital heart anomalies assisted at the Pediatric Cardiology Outpatient Department at the distinguished Western Paulista reference hospital. We conducted a retrospective study involving the analysis of electronic database records and patient medical charts for individuals diagnosed with congenital heart disease during the period from July 2013 to July 2018. A total of 298 medical records were selected for the analysis of variables encompassing the ICD-10 codes, gender, spatial distribution, and temporal trends. It was possible to observe that septal defects were the most prevalent congenital heart abnormalities, and there was no gender-based difference. An increase in diagnoses was noted from 2014, coinciding with the implementation of the "heart test," and 51% of the cases were from Presidente Prudente, with a higher concentration of cases in the industrial park area. There is an association between cardiac congenital malformations and an adverse environmental context. The findings can inform public health policies aimed at reducing the exposure of the most vulnerable population in pursuit of improving health indicators (AU).

O objetivo deste trabalho foi analisar a distribuição espaço-temporal dos pacientes com cardiopatias congênitas aten-didos no Ambulatório de Cardiologia Pediátrica do Hospital de referência do Oeste Paulista. Realizamos um estudo retrospectivo com análise de dados de base eletrônica e prontuários dos pacientes diagnosticados com cardiopatia congênita entre os períodos de julho de 2013 a julho de 2018. Foram selecionados 298 prontuários para análise das variáveis de CID-10, gênero, distribuição espacial e série temporal. Foi possível observar que os defeitos septais foram as cardiopatias mais prevalentes, não houve diferença entre os gêneros. Notou-se aumento do diagnóstico a partir de 2014, com implementação do teste do coraçãozinho e 51% dos casos eram da cidade de Presidente Prudente, com maior concentração de casos na região do parque industrial. Há uma relação na incidência das malformações cardíacas com o meio ambiente desfavorável. Os resultados encontrados podem guiar políticas de saúde pública, visando reduzir a exposição da população mais vulnerável, na busca da melhora nos índices de saúde (AU).

Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network.

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.

Climatic seasonality and water quality in watersheds: a study case in Limoeiro River watershed in the western region of São Paulo State, Brazil.

Applying the climatological water balance (WB) concept to describe the relationship between climatic seasonality and surface water quality according to different forms of land use and land cover (LULC) is an important issue, but little explored in the literature. In this paper, we evaluate the influence of WB on surface water quality and its impacts when interacting with LULC. We monitored 11 sampling points during the four seasons of the year, from which we estimate WQI (water quality index) and TSI (trophic state index). We found an effect of the seasonality factor on both WQI values (F(3,30) = 12.472; p < 0.01) and in TSI values (F(3,30) = 6.967; p < 0.01). We noticed that LULC interferes in the way that the water balance influences the WQI and TSI values since in sampling points closest to higher urban density, with little or no riparian protection, the correlation between water balance and water quality was lower. In the stations that had the lowest water surplus and deficit, there was positive linearity between water balance and WQI. However, in the seasons when the surplus and water deficit recorded were extreme, there was no linearity. We conclude that water deficiency impairs the quality of surface water. In the extreme surplus water season, the homogeneity of WQI samples was lower, suggesting a higher interaction between rainwater and LULC. This study contributes to design management strategies of water resources, considering the climatic seasonality for optimization.

Mother-to-child transmission and gestational syphilis: Spatial-temporal epidemiology and demographics in a Brazilian region.

Syphilis is a Sexually Transmitted Infection (IST) with significant importance to public health, due to its impact during pregnancy (Gestational Syphilis-GS); especially because syphilis can affect fetus and neonates' development (mother-to-child transmission-MTCT of syphilis), by increasing susceptibility to abortion, premature birth, skeletal malformations, meningitis and pneumonia. Measures to control and eliminate MTCT of syphilis have failed on the last few years in Brazil and this research aimed to identify the seasonality of notified cases of syphilis in a region of São Paulo state. The studied region, Pontal do Paranapanema, comprises 32 cities located in the West of São Paulo state, in Brazil. Data collected from the National System of Aggravations and Notification (SINAN) website was used to calculate the incidence rate of GS and MTCT. The incidence rate of GS was acquired dividing number of cases by number of women in each municipality and MTCT using number of live births in each year (from 2007 to 2013) in each municipality. This result was then, standardized multiplying incidence rate by 10,000 and expressed as incidence/10,000 women or live births, for GS and MTCT, respectively. To identify possible endemic/epidemic periods, a control diagram was performed using the standard deviation (SD) of incidence rate. Thematic maps representing the spatial distribution of incidence rates were constructed using a Geographic Information System software (GIS, based on cartographic vector available on the Brazilian Institute of Geography and Statistics (IBGE) website. Eighty cases of GS and 61 cases of MTCT were notified in the studied region. An increase of GS notification was detected in the Pontal do Paranapanema in 2011 followed by an increase in number of MTCT cases in the subsequent year, suggesting inefficacy in the treatment during gestational period. Most of those cases were reported on February and November which suggested seasonality for this IST in the region. The control diagram, based on the inputs collected from SINAN, showed no endemic period; however, the most susceptible month to happen an endemic event of GS and MTCT was February. Our study provided a new methodology to understand the syphilis dynamics as a potential tool to improve the success of future measures to control and possibly eliminate MTCT of syphilis.