Anomalous roughness with system size dependent local roughness exponent

We note that in a system far from equilibrium the interface roughening may depend on the system size which plays the role of control parameter. To detect the size effect on the interface roughness, we study the scaling properties of rough interfaces formed in paper combustion experiments. Using paper sheets of different width \lambda L, we found that the turbulent flame fronts display anomalous multi-scaling characterized by non universal global roughness exponent \alpha and the system size dependent spectrum of local roughness exponents,\xi_q, whereas the burning fronts possess conventional multi-affine scaling. The structure factor of turbulent flame fronts also exhibit unconventional scaling dependence on \lambda These results are expected to apply to a broad range of far from equilibrium systems, when the kinetic energy fluctuations exceed a certain critical value.Comment: 33 pages, 16 figure

Sheep Updates 2008 - part 3

This session covers fiveteen papers from different authors: CONTROLLING FLY STRIKE 1. Breeding for Blowfly Resistance - Indicatoe Traits, LJE Karlsson, JC Greeff, L Slocombe, Department of Agriculture & Food, Western Australia 2.A practical method to select for breech strike resistance in non-pedigreed Merino flocks, LJE Karlsson, JC Greeff, L Slocombe, K. Jones, N. Underwood, Department of Agriculture & Food, Western Australia 3. Twice a year shearing - no mulesing, Fred Wilkinson, Producer, Brookton WA BEEF 4. Commercial testing of a new tool for prediction of fatness in beef cattle, WD HoffmanA, WA McKiernanA, VH OddyB, MJ McPheeA, Cooperative Research Centre for Beef Genetic Technologies, A N.S.W. Deptartment of Primary Industries, B University of New England 5. A new tool for the prediction of fatness in beef cattle, W.A. McKiernanA, V.H. OddyB and M.J. McPheeC; Cooperative Research Centre for Beef Genetic Technologies, A N.S.W. Dept of Primary Industries, B University of New England, C N.S.W. Dept of Primary Industries Beef Industry Centre of Excellence. 6. Effect of gene markers for tenderness on eating quality of beef, B.L. McIntyre, CRC for Beef Genetic Technologies, Department of Agriculture and Food WA 7. Accelerating beef industry innovation through Beef Profit Partnerships, Parnell PF1,2, Clark RA1,3, Timms J1,3, Griffith G1,2, Alford A1,2, Mulholland C1 and Hyland P1,4,1Co-operative Research Centre for Beef Genetic Technologies; 2NSW Department of Primary Industries; 3 Qld Department of Primary Industries and Fisheries; 4The University of Queensland. SUSTAINABILITY 8. The WA Sheep Industry - is it ethically and environmentally sustainable? Danielle England, Department of Agriculture and Food Western Australia 9. Overview of ruminant agriculture and greenhouse emissions, Fiona Jones, Department of Agriculture and Food Western Australia 10. Grazing for Nitrogen Efficiency, John Lucey, Martin Staines and Richard Morris, Department of Agriculture and Food Western Australia 11. Investigating potential adaptations to climate change for low rainfall farming system, Megan Abrahams, Caroline Peek, Dennis Van Gool, Daniel Gardiner, Kari-Lee Falconer, Department of Agriculture and Food Western Australia SHEEP 12. Benchmarking ewe productivity through on-farm genetic comparisons, Sandra Prosser, Mario D’Antuono and Johan Greeff; Department of Agriculture and Food Western Australia 13. Increasing profitability by pregnancy scanning ewes, John Young1, Andrew Thompson2 and Chris Oldham2; 1Farming Systems Analysis Service, Kojonup, WA, 2Department of Agriculture and Food Western Australia 14. Targeted treatment of worm-affected sheep - more efficient, more sustainable, Brown Besier, Department of Agriculture and Food Western Australia 15. Improving Weaner Sheep Survival, Angus Campbell and Ralph Behrendt, Cooperative Research Centre for Sheep Industry Innovatio

Implementation of the COVID-19 vulnerability index across an international network of health care data sets:Collaborative external validation study

Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.</p

Crop Updates 2009 - Farming Systems

This session covers nineteen papers from different authors: Decision support technology 1. The use of high resolution imagery in broad acre cropping, Derk Bakker and Grey Poulish, Department of Agriculture and Food 2. Spraywise decisions – online spray applicatiors planning tool, Steve Lacy, Nufarm Australia Ltd 3. Testing for redlegged earthmite resistance in Western Australia, Svetlana Micic, Peter Mangano, Tony Dore and Alan Lord, Department of Agriculture and Food 4. Screening cereal, canola and pasture cultivars for Root Lesion Nematode (Pratylenchus neglectus), Vivien Vanstone, Helen Hunter and Sean Kelly,Department of Agriculture and Food Farming Systems Research 5. Lessons from five years of cropping systems research, WK Anderson, Department of Agriculture and Food 6. Facey Group rotations for profit: Five years on and where to next? Gary Lang and David McCarthy, Facey Group, Wickepin, WA Mixed Farming 7. Saline groundwater use by Lucerne and its biomass production in relation to groundwater salinity, Ruhi Ferdowsian, Ian Roseand Andrew Van Burgel, Department of Agriculture and Food 8. Autumn cleaning yellow serradella pastures with broad spectrum herbicides – a novel weed control strategy that exploits delayed germination, Dr David Ferris, Department of Agriculture and Food 9. Decimating weed seed banks within non-crop phases for the benefit of subsequent crops, Dr David Ferris, Department of Agriculture and Food 10. Making seasonal variability easier to deal with in a mixed farming enterprise! Rob Grima,Department of Agriculture and Food 11. How widely have new annual legume pastures been adopted in the low to medium rainfall zones of Western Australia? Natalie Hogg, Department of Agriculture and Food, John Davis, Institute for Sustainability and Technology Policy, Murdoch University 12. Economic evaluation of dual purpose cereal in the Central wheatbelt of Western Australia, Jarrad Martin, Pippa Michael and Robert Belford, School of Agriculture and Environment, CurtinUniversity of Technology, Muresk Campus 13. A system for improving the fit of annual pasture legumes under Western Australian farming systems, Kawsar P Salam1,2, Roy Murray-Prior1, David Bowran2and Moin U. Salam2, 1Curtin University of Technology; 2Department of Agriculture and Food 14. Perception versus reality: why we should measure our pasture, Tim Scanlon, Department of Agriculture and Food, Len Wade, Charles Sturt University, Megan Ryan, University of Western Australia Modelling 15. Potential impact of climate changes on the profitability of cropping systems in the medium and high rainfall areas of the northern wheatbelt, Megan Abrahams, Chad Reynolds, Caroline Peek, Dennis van Gool, Kari-Lee Falconer and Daniel Gardiner, Department of Agriculture and Food 16. Prediction of wheat grain yield using Yield Prophet®, Geoff Anderson and Siva Sivapalan, Department of Agriculture and Food 17. Using Yield Prophet® to determine the likely impacts of climate change on wheat production, Tim McClelland1, James Hunt1, Zvi Hochman2, Bill Long3, Dean Holzworth4, Anthony Whitbread5, Stephen van Rees1and Peter DeVoil6 1 Birchip Cropping Group, Birchip, Vic, 2Agricultural Production Systems Research Unit (APSRU), CSIRO Sustainable Ecosystems, Climate Adaptation Flagship, Qld, 3 AgConsulting, SA 4 Agricultural Production Systems Research Unit (APSRU), CSIRO Sustainable Ecosystems, Toowoomba Qld, 5 CSIRO Sustainable Ecosystems, SA, 6 Agricultural Production Systems Research Unit (APSRU), Department of Agriculture and Fisheries, Queensland 18. Simple methods to predict yield potential: Improvements to the French and Schultz formula to account for soil type and within-season rainfall, Yvette Oliver, Michael Robertson and Peter Stone, CSIRO Sustainable Ecosystems 19. Ability of various yield forecasting models to estimate soil water at the start of the growing season, Siva Sivapalan, Kari-Lee Falconer and Geoff Anderson, Department of Agriculture and Foo

Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci