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ABSTRACT:It is often difficult for pedologists to "see" topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only "see" these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities.

Poly(L-lactic acid) (PLLA) and poly(ethylene oxide) (PEO) blends were prepared by mechanical mixture and fusion of homopolymers. Samples were submitted to in vitro degradation tests (immersion in a phosphate buffer solution with pH = 7.4 at 37 °C). Independently of the blend composition, PEO was dissolved after 14 days of immersion. As expected, after immersion, scanning electron microscopy showed that the blends were porous, contrary to the samples, which were not immersed in the buffer solution. Phase separation was not evident. Using differential scanning calorimetry, the melting points (Tm) of both PLLA and PEO crystalline fractions were observed and remained practically constant, indicating no miscibility. Thermogravimetry showed that the temperature where the main mass loss stage starts (Tonset), depended on the blend composition and period of immersion in the buffer. The blends and the PLLA homopolymer were implanted in defects produced in the tibias of rats. The blends were as biocompatible as the PLLA

Quercetin has potent antioxidant action and a hepatoprotective role. The aim of this study was to evaluate the hepatoprotective action of quercetin pretreatment in paracetamol-induced liver damage (PILD) and structural injury resulting from partial hepatectomy (PH

Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.