Incidence of hemoglobinopathies and spatialization of newborns with sickle cell trait in Mato Grosso do Sul, Brazil.

OBJECTIVE: To evaluate the incidence of variant hemoglobins of newborn samples from the Neonatal Screening Center in the state of Mato Grosso do Sul, Brazil, and to analyze the distribution and spatial autocorrelation of newborns with sickle cell trait. METHODS: Samples from 35,858 newborns screened by the Neonatal Screening Center. The samples with inconclusive diagnosis were submitted to electrophoretic, chromatographic, cytological and molecular analyses. The spatial distribution analysis of newborns with sickle cell trait was performed by spatial autocorrelation. RESULTS: A total of 919 newborns showed an abnormal hemoglobin profile; in that, ten genotypes had significant clinical impacts identified. Among the asymptomatic newborns, the sickle cell trait was the most frequent (incidence of 1.885 cases/100 newborns). The highest incidence rates were registered in the municipalities of Terenos, Figueirão, Corguinho and Selvíria. There was positive spatial autocorrelation between the proportion of declared individuals of black race/color and the incidence of newborns with sickle cell trait. CONCLUSION: The early diagnosis by neonatal screening and laboratory tests was very important to identify abnormal hemoglobin profiles and guide the spatial autocorrelation analysis of sickle cell trait newborns in Mato Grosso do Sul, serving as a support to anticipate health measures aimed to discuss efficient therapeutic behaviors and effective planning of municipalities with the greatest need for care, monitoring and orientations for affected families.

Incidence of hemoglobinopathies and spatialization of newborns with sickle cell trait in Mato Grosso do Sul, Brazil

ABSTRACT Objective To evaluate the incidence of variant hemoglobins of newborn samples from the Neonatal Screening Center in the state of Mato Grosso do Sul, Brazil, and to analyze the distribution and spatial autocorrelation of newborns with sickle cell trait. Methods Samples from 35,858 newborns screened by the Neonatal Screening Center. The samples with inconclusive diagnosis were submitted to electrophoretic, chromatographic, cytological and molecular analyses. The spatial distribution analysis of newborns with sickle cell trait was performed by spatial autocorrelation. Results A total of 919 newborns showed an abnormal hemoglobin profile; in that, ten genotypes had significant clinical impacts identified. Among the asymptomatic newborns, the sickle cell trait was the most frequent (incidence of 1.885 cases/100 newborns). The highest incidence rates were registered in the municipalities of Terenos, Figueirão, Corguinho and Selvíria. There was positive spatial autocorrelation between the proportion of declared individuals of black race/color and the incidence of newborns with sickle cell trait. Conclusion The early diagnosis by neonatal screening and laboratory tests was very important to identify abnormal hemoglobin profiles and guide the spatial autocorrelation analysis of sickle cell trait newborns in Mato Grosso do Sul, serving as a support to anticipate health measures aimed to discuss efficient therapeutic behaviors and effective planning of municipalities with the greatest need for care, monitoring and orientations for affected families.

Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil.

In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32 km2 of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (- 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. Graphical abstract.

Rotinas de tratamento digital de imagem Landsat 5/TM para a identificação de queimadas em lavouras canavieiras / Handling Landsat 5/TM digital image to identify burned in sugarcane plantations

O presente estudo teve como objetivo avaliar o desempenho de classificadores supervisionados e não-supervisionados para detecção automática de queimadas em canaviais utilizando imagens de satélite Landsat-5/TM. A área de estudo localiza-se na porção noroeste do município de Maracaju, MS, Brasil. Diferentes métodos de classificação e tratamento de imagem foram testados para mapear a colheita de cana com queima prévia de palha. As imagens foram tratadas com reamostragem para 15m, correção radiométrica e NDVI. Nas classificações, foram utilizados os algoritmos Maxver-ICM, Bhattacharya e ISOSEG. Os diferentes pré-processamentos e classificadores aplicados foram submetidos à validação estatística por meio dos parâmetros Kappa e exatidão global. Os resultados indicaram um expressivo potencial de classificadores supervisionados na identificação de queimadas de cana. Concluiu-se que é possível obter exatidões qualificadas como excelente quando empregado o classificador de Máxima Verossimilhança-ICM.

The present study aimed to evaluate the performance of supervised classifiers and unsupervised for automatic detection of fires in cane fields using satellite images Landsat-5/TM. The study area is located in the northwest from the town of Maracaju, state of Mato Grosso do Sul, Brazil. Different methods of classification and image processing were tested to map the cane harvesting prior to straw burning. The images were treated with resampling to 15m, radiometric correction and NDVI. The classifications were used algorithms Maxver-ICM, Bhattacharya and ISOSEG. The different pre-processing and applied classifiers were submitted to statistical validation through the parameters Kappa and overall accuracy. The results indicated a significant potential for supervised classifiers in identifying burnt cane. It was concluded that it is possible to obtain accuracies classified as excellent when used the Maximum Likelihood Classifier-ICM.