a b s t r a c tMODIS 250-m NDVI and EVI datasets are now regularly used to classify regional-scale agricultural land-use practices in many different regions of the globe, especially in the state of Mato Grosso, Brazil, where rapid land-use change due to agricultural development has attracted considerable interest from researchers and policy makers. Variation exists in which MODIS datasets are used, how they are processed for analysis, and what ground reference data are used. Moreover, various land-use/land-cover classes are ultimately resolved, and as yet, crop-specific classifications (e.g. soy-corn vs. soy-cotton double crop) have not been reported in the literature, favoring instead generalized classes such as single vs. double crop. The objective of this study is to present a rigorous multiyear evaluation of the applicability of time-series MODIS 250-m VI data for crop classification in Mato Grosso, Brazil. This study shows progress toward more refined crop-specific classification, but some grouping of crop classes remains necessary. It employs a farm field polygon-based ground reference dataset that is unprecedented in spatial and temporal coverage for the state, consisting of 2003 annual field site samples representing 415 unique field sites and five crop years (2005)(2006)(2007)(2008)(2009)). This allows for creation of a dataset containing "best-case" or "pure" pixels, which we used to test class separability in a multiyear cross validation framework applied to boosted decision tree classifiers trained on MODIS data subjected to different pre-processing treatments. Reflecting the agricultural landscape of Mato Grosso as a whole, cropping practices represented in the ground reference dataset largely involved soybeans, and soy-based classes (primarily double crop 'soy-commercial' and single crop 'soy-cover') dominated the analysis along with cotton and pasture. With respect to the MODIS data treatments, the best results were obtained using date-ofacquisition interpolation of the 16-day composite VI time series and outlier point screening, for which five-year out-of-sample accuracies were consistently near or above 80% and Kappa values were above 0.60. It is evident that while much additional research is required to fully and reliably differentiate more specific crop classes, particular groupings of cropping strategies are separable and useful for a number of applications, including studies of agricultural intensification and extensification in this region of the world.

Previous research has established the usefulness of remotely sensed vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to characterize the spatial dynamics of agriculture in the state of Mato Grosso (MT), Brazil. With these data it has become possible to track MT agriculture, which accounts for ~85% of Brazilian Amazon soy production, across periods of several years. Annual land cover (LC) maps support investigation of the spatiotemporal dynamics of agriculture as they relate to forest cover and governance and policy efforts to lower deforestation rates. We use a unique, spatially extensive 9-year (2005–2013) ground reference dataset to classify, with approximately 80% accuracy, MODIS VI data, merging the results with carefully processed annual forest and sugarcane coverages developed by Brazil’s National Institute for Space Research to produce LC maps for MT for the 2001–2014 crop years. We apply the maps to an evaluation of forest and agricultural intensification dynamics before and after the Soy Moratorium (SoyM), a governance effort enacted in July 2006 to halt deforestation for the purpose of soy production in the Brazilian Amazon. We find the pre-SoyM deforestation rate to be more than five times the post-SoyM rate, while simultaneously observing the pre-SoyM forest-to-soy conversion rate to be more than twice the post-SoyM rate. These observations support the hypothesis that SoyM has played a role in reducing both deforestation and subsequent use for soy production. Additional analyses explore the land use tendencies of deforested areas and the conceptual framework of horizontal and vertical agricultural intensification, which distinguishes production increases attributable to cropland expansion into newly deforested areas as opposed to implementation of multi-cropping systems on existing cropland. During the 14-year study period, soy production was found to shift from predominantly single-crop systems to majority double-crop systems.

There is no role for standard automated oscillometric devices in the calculation of ABPI in the vascular clinic.

The CC chemokine macrophage inflammatory protein 1α (MIP1α) is a key regulator of the proliferation and differentiation of hematopoietic progenitor cells. The activity of MIP1α appears to be modulated by its binding to heparan sulfate (HS) proteoglycans, ubiquitous components of the mammalian cell surface and extracellular matrix. In this study we show that HS has highest affinity for the dimeric form of MIP1α. The predominantly dimeric BB10010 MIP1α interacts with an 8.3-kDa sequence in the HS polysaccharide chain, which it protects from degradation by heparinase enzymes. The major structural motif of this HS fragment appears to consist of 2 sulfate-rich S-domains separated by a short central N-acetylated region. The optimum lengths of these S-domains seem to be 12 to 14 saccharides. We propose that this binding fragment may wrap around the MIP1α dimer in a horseshoe shape, facilitating the interaction of the S-domains with the heparin-binding domains on each monomer. Molecular modeling suggests that these S-domains are likely to interact with basic residues Arg 17, Arg 45, and Arg 47 and possibly with Lys 44 on MIP1α and that the interconnecting N-acetylated region is of sufficient length to allow the 2 S-domains to bind to these sites on opposite faces of the dimer. Elucidation of the structure of the HS-binding site for MIP1α may enable us to devise ways of enhancing its myeloprotective or peripheral blood stem cell mobilization properties, which can be used to improve cancer chemotherapy treatments.