Effects of Sheep Grazing Systems on Water Quality with a Focus on Nitrate Leaching

This article reviews the literature on nitrate leaching under sheep grazing systems and focuses on identifying future research needs. Urinary nitrogen (N) is an important source of the nitrate leached from pastoral agriculture. Urinary N excretion can be measured or simulated using models and has been well characterised for dairy systems. It is difficult to continuously monitor the urinary N excretion of sheep under field conditions; consequently, measurements of N excretion in sheep urine are limited. Urination events by sheep vary greatly in volume (0.5 L to 6.9 L), concentration (3 to 13.7 g N/L), and frequency (8 to 23 events/day); this variation results in a corresponding variation in N loading rates in urine patches. The amount of nitrate leached under pastures grazed by sheep has typically varied between 1 and 50 kg N/ha/year, but rates as high as 300 kg N/ha/year have been reported. The quantity of nitrate leached under sheep depends on the season, climate, quantity and timing of drainage, the interaction between forage production and stocking rate, fertiliser applied, N fixation by legumes, forage type, and grazing management. The majority of studies examining nitrate leaching under sheep grazing systems are more than 20 years old; so, there is little recent information on nitrate leaching under modern pasture-based sheep production systems. Further research is required to quantify nitrate leaching levels under current sheep farming practices, to understand the impacts of this leaching on water quality, and to help identify effective strategies to reduce the transfer of N from grazed paddocks to receiving water bodies. This additional information will help provide information for decision support tools, including models and management practices, to help sheep farmers minimise their impact on the aquatic environment.fals

Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks

A state-of-the-art data analysis procedure is presented to conduct hierarchical Bayesian inference and hypothesis testing on delay discounting data. The delay discounting task is a key experimental paradigm used across a wide range of disciplines from economics, cognitive science, and neuroscience, all of which seek to understand how humans or animals trade off the immediacy verses the magnitude of a reward. Bayesian estimation allows rich inferences to be drawn, along with measures of confidence, based upon limited and noisy behavioural data. Hierarchical modelling allows more precise inferences to be made, thus using sometimes expensive or difficult to obtain data in the most efficient way. The proposed probabilistic generative model describes how participants compare the present subjective value of reward choices on a trial-to-trial basis, estimates participant- and group-level parameters. We infer discount rate as a function of reward size, allowing the magnitude effect to be measured. Demonstrations are provided to show how this analysis approach can aid hypothesis testing. The analysis is demonstrated on data from the popular 27-item monetary choice questionnaire (Kirby, Psychonomic Bulletin & Review, 16(3), 457–462 2009), but will accept data from a range of protocols, including adaptive procedures. The software is made freely available to researchers

Active learning and optimal climate policy

This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education

In vivo Hypoxia and a Fungal Alcohol Dehydrogenase Influence the Pathogenesis of Invasive Pulmonary Aspergillosis

Currently, our knowledge of how pathogenic fungi grow in mammalian host environments is limited. Using a chemotherapeutic murine model of invasive pulmonary aspergillosis (IPA) and 1H-NMR metabolomics, we detected ethanol in the lungs of mice infected with Aspergillus fumigatus. This result suggests that A. fumigatus is exposed to oxygen depleted microenvironments during infection. To test this hypothesis, we utilized a chemical hypoxia detection agent, pimonidazole hydrochloride, in three immunologically distinct murine models of IPA (chemotherapeutic, X-CGD, and corticosteroid). In all three IPA murine models, hypoxia was observed during the course of infection. We next tested the hypothesis that production of ethanol in vivo by the fungus is involved in hypoxia adaptation and fungal pathogenesis. Ethanol deficient A. fumigatus strains showed no growth defects in hypoxia and were able to cause wild type levels of mortality in all 3 murine models. However, lung immunohistopathology and flow cytometry analyses revealed an increase in the inflammatory response in mice infected with an alcohol dehydrogenase null mutant strain that corresponded with a reduction in fungal burden. Consequently, in this study we present the first in vivo observations that hypoxic microenvironments occur during a pulmonary invasive fungal infection and observe that a fungal alcohol dehydrogenase influences fungal pathogenesis in the lung. Thus, environmental conditions encountered by invading pathogenic fungi may result in substantial fungal metabolism changes that influence subsequent host immune responses

The effect of planting date on maize: Silage yield, starch content and leaf area

Four field experiments were established in the Waikato and Manawatu regions over two years to determine planting date (PD) influence on growth, silage yield (SY) and starch content of seven maize (Zea mays) hybrids. Silage yield response to PD was best described using quadratic regression models. The PD at which silage yield was maximised (optimum PD) was later in the cooler, high latitude environment of Manawatu (23 October) than the more northerly locations in Waikato (9-15 October). In both regions, planting 2 or 3 weeks either side of the optimum PD reduced SY by <5%. In Waikato, the optimum PD in a warmer than average spring (+1°C) was 1-2 weeks earlier. Under non-limiting moisture conditions later planting reduced yields in both Waikato (24.22 versus 21.06 t/ha) and Manawatu (30.09 versus 22.50 t/ha). This was attributed to decreased temperatures (<15°C) and radiation (<17 MJ/m2 /d) during grain filling. Due to more rapid reductions in autumn temperature and radiation in Manawatu, yield decline beyond the optimum PD was greater (-183 kg/ha/d (0.6%), R 2 =0.81) than Waikato (-50 to -85 kg/ha/d (0.3%), R 2 ≥0.67). Starch content was highest for plantings before 6 November, dropping thereafter with harvest index. Highest maximum leaf area index was observed at mean daily temperatures of 17-19°C.falsefalsefalsefalsefalsefalsefalsefals

Abstract P5-03-10: Development of a novel HER2 testing strategy, using image-based cell-sorting to isolate pure cell populations from FFPE upstream of FISH

Abstract Fluorescent in Situ Hybridization (FISH) guidelines defined by American Society of Clinical Oncology (ASCO) and the College of American Pathologists for determining HER2 status are set to improve accuracy and usefulness as a diagnostic marker in breast cancer. Despite these guidelines, many factors can influence HER2 testing results such as sample preparation, assay-conditions and interpretation of test results due to heterogeneous breast cancer samples. In this multi-site study, sample preparation was carried out using the DEPArray™ to recover pure tumor cell populations from formalin-fixed, paraffin-embedded (FFPE) breast tumor samples. We then compared HER2/CEP17 ratios obtained from the DEPArray™ processed samples from each laboratory to routine FISH on tissue sections. Methods: Eight breast FFPE tumor tissue biopsies were obtained from commercial tissue banks. From the paraffin tissue blocks, four consecutive tissue curls (each 50 microns thick) were prepared. One curl from each of the 8 patient samples was distributed to four different laboratories for analysis following DEPArray™ based sample preparation. After an initial disassociation of each curl into a single-cell suspension, intact cells were sorted and then recovered based on cytokeratin/ vimentin/DAPI staining using the DEPArray™. Cytokeratin+/Vimentin-/DAPI+ tumor (~250) and Cytokeratin-/Vimentin+/DAPI+ stromal (~250) recovered cells were then deposited onto glass slides prior to standard dual-color HER2/CEP17 FISH analysis for comparison to conventional HER2 FISH result. Results: Serially sectioned breast tumors from 8 negative/positive cases: 7 infiltrating ductal carcinoma (IDC) and 1 metastatic carcinoma were studied. All four sites demonstrated 100% concordance between FISH results compared to the conventional HER2 FISH result. Overall, &amp;gt;60% of DEPArray™ isolated cells were recovered from FFPE samples that ranged from 1- 15 years of age and reported to contain 60% to 80% tumor content. The use of pure sorted cells permitted the accurate determination of HER2 amplification status in only the tumor cells while the stromal cells consistently yielded a more normalized ratio of HER2 to centromere 17. Conclusion: The preliminary results of this multi-site study demonstrate that use of DEPArray™ for sorted pure populations is reproducible as well as reliable method for subsequent analysis of HER2 by FISH on FFPE derived tumor cells. Given that traditional FFPE-based HER2 FISH results may be influenced by the tissue sectioning procedure, tissue heterogeneity and/or the scattering of few HER2 amplified tumor cells among normal stromal cells. The DEPArray™ allows analysis of immunofluorescence images and DNA content to isolate and recover pure and intact cell populations. This isolation of pure cell populations prior to FISH analysis is attractive for achieving precise determination of HER2 status on equivocal cases. A more formal analytical validation of this approach through CLIA is currently underway. Citation Format: Gerber A, Konig L, Millner L, Strotoman L, Khurana A, Kasimir-Bauer S, Moore MW, Cotter PD, Bischoff F. Development of a novel HER2 testing strategy, using image-based cell-sorting to isolate pure cell populations from FFPE upstream of FISH [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-03-10.</jats:p