Endometrial cells sense and react to tissue damage during infection of the bovine endometrium via interleukin 1

Cells generate inflammatory responses to bacteria when pattern recognition receptors bind pathogen-associated molecules such as lipopolysaccharide. Cells may also respond to tissue damage by sensing damage-associated molecules. Postpartum bacterial infections of the bovine uterus cause endometritis but the risk of disease is increased by tissue trauma triggered by dystocia. Animals that suffered dystocia had increased concentrations of inflammatory mediators IL-8, IL-1β and IL-1α in vaginal mucus 3 weeks postpartum, but they also had more bacteria than normal animals. Ex vivo organ cultures of endometrium, endometrial cells and peripheral blood monocytes did not generate inflammatory responses to prototypical damage molecules, HMGB1 or hyaluronan, or to necrotic cells; although they secreted IL-6 and IL-8 in a concentration-dependent manner when treated with IL-1α. However, necrotic endometrial cells did not accumulate intracellular IL-1α or release IL-1α, except when pre-treated with lipopolysaccharide or bacteria. Endometrial cell inflammatory responses to IL-1α were dependent on the cognate receptor IL-1R1, and the receptor adaptor protein MyD88, and the inflammatory response to IL-1α was independent of the response to lipopolysaccharide. Rather than a typical damage-associated molecule, IL-1α acts to scale the inflammatory response in recognition that there is a combination of pathogen challenge followed by endometrial cell damage

Mapping and simulating systematics due to spatially-varying observing conditions in DES Science Verification data

Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, in particular in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. We illustrate the complementarity of these two approaches by comparing the SV data with the BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES SV images. We analyse the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially-varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and well-captured by the maps of observing conditions. The combined use of the maps, the SV data and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on $N(z)$, the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak lensing analyses. The framework presented here is relevant to all multi-epoch surveys, and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope (LSST), which will require detailed null-tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky

Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data

Shear peak statistics has gained a lot of attention recently as a practical alternative to the two point statistics for constraining cosmological parameters. We perform a shear peak statistics analysis of the Dark Energy Survey (DES) Science Verification (SV) data, using weak gravitational lensing measurements from a 139 deg$^2$ field. We measure the abundance of peaks identified in aperture mass maps, as a function of their signal-to-noise ratio, in the signal-to-noise range $04$ would require significant corrections, which is why we do not include them in our analysis. We compare our results to the cosmological constraints from the two point analysis on the SV field and find them to be in good agreement in both the central value and its uncertainty. We discuss prospects for future peak statistics analysis with upcoming DES data

Insurance data for research in companion animals: benefits and limitations

The primary aim of this article is to review the use of animal health insurance data in the scientific literature, especially in regard to morbidity or mortality in companion animals and horses. Methods and results were compared among studies on similar health conditions from different nations and years. A further objective was to critically evaluate benefits and limitations of such databases, to suggest ways to maximize their utility and to discuss the future use of animal insurance data for research purposes. Examples of studies on morbidity, mortality and survival estimates in dogs and horses, as well as neoplasia in dogs, are discussed

IL-1α Signaling Is Critical for Leukocyte Recruitment after Pulmonary Aspergillus fumigatus Challenge

Aspergillus fumigatus is a mold that causes severe pulmonary infections. Our knowledge of how A. fumigatus growth is controlled in the respiratory tract is developing, but still limited. Alveolar macrophages, lung resident macrophages, and airway epithelial cells constitute the first lines of defense against inhaled A. fumigatus conidia. Subsequently, neutrophils and inflammatory CCR2+ monocytes are recruited to the respiratory tract to prevent fungal growth. However, the mechanism of neutrophil and macrophage recruitment to the respiratory tract after A. fumigatus exposure remains an area of ongoing investigation. Here we show that A. fumigatus pulmonary challenge induces expression of the inflammasome-dependent cytokines IL-1β and IL-18 within the first 12 hours, while IL-1α expression continually increases over at least the first 48 hours. Strikingly, Il1r1-deficient mice are highly susceptible to pulmonary A. fumigatus challenge exemplified by robust fungal proliferation in the lung parenchyma. Enhanced susceptibility of Il1r1-deficient mice correlated with defects in leukocyte recruitment and anti-fungal activity. Importantly, IL-1α rather than IL-1β was crucial for optimal leukocyte recruitment. IL-1α signaling enhanced the production of CXCL1. Moreover, CCR2+ monocytes are required for optimal early IL-1α and CXCL1 expression in the lungs, as selective depletion of these cells resulted in their diminished expression, which in turn regulated the early accumulation of neutrophils in the lung after A. fumigatus challenge. Enhancement of pulmonary neutrophil recruitment and anti-fungal activity by CXCL1 treatment could limit fungal growth in the absence of IL-1α signaling. In contrast to the role of IL-1α in neutrophil recruitment, the inflammasome and IL-1β were only essential for optimal activation of anti-fungal activity of macrophages. As such, Pycard-deficient mice are mildly susceptible to A. fumigatus infection. Taken together, our data reveal central, non-redundant roles for IL-1α and IL-1β in controlling A. fumigatus infection in the murine lung

Sugarcane (Saccharum X officinarum): A Reference Study for the Regulation of Genetically Modified Cultivars in Brazil

Global interest in sugarcane has increased significantly in recent years due to its economic impact on sustainable energy production. Sugarcane breeding and better agronomic practices have contributed to a huge increase in sugarcane yield in the last 30 years. Additional increases in sugarcane yield are expected to result from the use of biotechnology tools in the near future. Genetically modified (GM) sugarcane that incorporates genes to increase resistance to biotic and abiotic stresses could play a major role in achieving this goal. However, to bring GM sugarcane to the market, it is necessary to follow a regulatory process that will evaluate the environmental and health impacts of this crop. The regulatory review process is usually accomplished through a comparison of the biology and composition of the GM cultivar and a non-GM counterpart. This review intends to provide information on non-GM sugarcane biology, genetics, breeding, agronomic management, processing, products and byproducts, as well as the current technologies used to develop GM sugarcane, with the aim of assisting regulators in the decision-making process regarding the commercial release of GM sugarcane cultivars

Microtopographic drivers of vegetation patterning in blanket peatlands recovering from erosion

Blanket peatlands are globally rare, and many have been severely eroded. Natural recovery and revegetation (‘self-restoration’) of bare peat surfaces are often observed but are poorly understood, thus hampering the ability to reliably predict how these ecosystems may respond to climatic change. We hypothesised that morphometric/topographic-related microclimatic variables may be key controls on successional pathways and vegetation patterning in self-restoring blanket peatlands. We predicted the occurrence probability of four common peatland plant species (Calluna vulgaris, Eriophorum vaginatum, Eriophorum angustifolium, and Sphagnum spp.) using a digital surface model (DSM) generated from drone imagery at a pixel size of 20 cm, a suite of variables derived from the DSM, and an ensemble learning method (random forests). All four species models provided accurate fine-scale predictions of habitat suitability (accuracy > 90%, area under curve (AUC) > 0.9, recall and precision > 0.8). Mean elevation (within a 1 m radius) was often the most influential variable. Topographic position, wind exposure, and the heterogeneity or ruggedness of the surrounding surface were also important for all models, whilst light-related variables and a wetness index were important in the Sphagnum model. Our approach can be used to improve prediction of future responses and sensitivities of peatland recovery to climatic changes and as a tool to identify areas of blanket peatlands that may self-restore successfully without management intervention

Dark Energy Survey Year 1 Results: Calibration of redMaGiC redshift distributions in DES and SDSS from cross-correlations

We present calibrations of the redshift distributions of redMaGiC galaxies in the Dark Energy Survey Year 1 (DES Y1) and Sloan Digital Sky Survey (SDSS) DR8 data. These results determine the priors of the redshift distribution of redMaGiC galaxies, which were used for galaxy clustering measurements and as lenses for galaxy-galaxy lensing measurements in DES Y1 cosmological analyses. We empirically determine the bias in redMaGiC photometric redshift estimates using angular cross-correlations with Baryon Oscillation Spectroscopic Survey (BOSS) galaxies. For DES, we calibrate a single parameter redshift bias in three photometric redshift bins: $z \in[0.15,0.3]$, [0.3,0.45], and [0.45,0.6]. Our best fit results in each bin give photometric redshift biases of $|\Delta z|<0.01$. To further test the redMaGiC algorithm, we apply our calibration procedure to SDSS redMaGiC galaxies, where the statistical precision of the cross-correlation measurement is much higher due to a greater overlap with BOSS galaxies. For SDSS, we also find best fit results of $|\Delta z|<0.01$. We compare our results to other analyses of redMaGiC photometric redshifts