Choosing ‘buy none’ in food choice analysis: the role of utility balance

Stated choice analysis is now a widely used and accepted methodology for exploring food choice. In stated choice experiments respondents are asked to make a choice between two or more alternatives, one of which typically takes the form of a ‘buy none’ option. It is widely recognised that respondents often perceive this option differently from the other alternatives and various reasons for this have been offered. Nevertheless, the role that utility balance among the experimentally designed options plays on the propensity of respondent’s choosing ‘buy none’ has largely been overlooked. Using a non-linear representation of utility we show that the ‘buy none’ choices are sensitive to utility balance. We further show how accommodating this provides an additional insight into choice behaviour and has a bearing on welfare calculationsdiscrete choice experiments, utility balance, status-quo bias, food choice, Consumer/Household Economics,

Healthcare resource use and costs of severe, uncontrolled eosinophilic asthma in the UK general population

Acknowledgments The authors thank Derek Skinner (Cambridge Research Support Ltd, Oakington, Cambridge, UK) for assistance with data extraction.Peer reviewedPublisher PD

Genetic Diversity and Competitive Abilities of Dalea Purpurea (Fabaceae) from Remnant and Restored Grasslands

Allozyme and randomly amplified polymorphic DNA (RAPD) analyses were used to characterize the genetic relationships of Dalea purpurea from remnant and restored Illinois tallgrass prairies and a large remnant tallgrass prairie in Kansas. The remnant Illinois populations were less genetically diverse than the restored Illinois populations and the Kansas population. These restored Illinois populations were established with at least two seed sources that were locally collected. There was little population divergence (Fst = 0.042), which ST is consistent with other perennial forbs, while the genetic relationships among populations reflected geographic proximity. In a greenhouse competition experiment, differences in performance between seedlings was not related to the remnant or restored status of Illinois populations, but plants from Kansas were significantly smaller than Illinois plants. Genetic diversity and competitive ability were not associated with the size of the original source population. Our data indicate that using multiple local seed sources for restoration projects will maintain the local gene pool while enhancing the regional genetic diversity of this species

Facts about our ecological crisis are incontrovertible: we must take action

Humans cannot continue to violate the fundamental laws of nature or science with impunity, say 94 signatories including Dr Alison Green and Molly Scott Cato MEP. Professor of Sustainability Leadership at the University of Cumbria Jem Bendell joined others in calling for a wider debate about sustainability, featured in The Guardian. We the undersigned represent diverse academic disciplines, and the views expressed here are those of the signatories and not their organisations. While our academic perspectives and expertise may differ, we are united on one point: we will not tolerate the failure of this or any other government to take robust and emergency action in respect of the worsening ecological crisis. The science is clear, the facts are incontrovertible, and it is unconscionable to us that our children and grandchildren should have to bear the terrifying brunt of an unprecedented disaster of our own making

No effect of seed source on multiple aspects of ecosystem functioning during ecological restoration: cultivars compared to local ecotypes of dominant grasses

Genetic principles underlie recommendations to use local seed, but a paucity of information exists on the genetic distinction and ecological consequences of using different seed sources in restorations. We established a field experiment to test whether cultivars and local ecotypes of dominant prairie grasses were genetically distinct and differentially influenced ecosystem functioning. Whole plots were assigned to cultivar and local ecotype grass sources. Three subplots within each whole plot were seeded to unique pools of subordinate species. The cultivar of the increasingly dominant grass, Sorghastrum nutans, was genetically different than the local ecotype, but genetic diversity was similar between the two sources. There were no differences in aboveground net primary production, soil carbon accrual, and net nitrogen mineralization rate in soil between the grass sources. Comparable productivity of the grass sources among the species pools for four years shows functional equivalence in terms of biomass production. Subordinate species comprised over half the aboveground productivity, which may have diluted the potential for documented trait differences between the grass sources to influence ecosystem processes. Regionally developed cultivars may be a suitable alternative to local ecotypes for restoration in fragmented landscapes with limited gene flow between natural and restored prairie and negligible recruitment by seed

Choosing 'buy none' in food choice analysis: the role of utility balance

Stated choice analysis is now a widely used and accepted methodology for exploring food choice. In stated choice experiments respondents are asked to make a choice between two or more alternatives, one of which typically takes the form of a 'buy none' option. It is widely recognised that respondents often perceive this option differently from the other alternatives and various reasons for this have been offered. Nevertheless, the role that utility balance among the experimentally designed options plays on the propensity of respondent's choosing 'buy none' has largely been overlooked. Using a non-linear representation of utility we show that the 'buy none' choices are sensitive to utility balance. We further show how accommodating this provides an additional insight into choice behaviour and has a bearing on welfare calculations

Effect of a Perioperative, Cardiac Output-Guided Hemodynamic Therapy Algorithm on Outcomes Following Major Gastrointestinal Surgery A Randomized Clinical Trial and Systematic Review

Importance: small trials suggest that postoperative outcomes may be improved by the use of cardiac output monitoring to guide administration of intravenous fluid and inotropic drugs as part of a hemodynamic therapy algorithm.Objective: to evaluate the clinical effectiveness of a perioperative, cardiac output–guided hemodynamic therapy algorithm.Design, setting, and participants: OPTIMISE was a pragmatic, multicenter, randomized, observer-blinded trial of 734 high-risk patients aged 50 years or older undergoing major gastrointestinal surgery at 17 acute care hospitals in the United Kingdom. An updated systematic review and meta-analysis were also conducted including randomized trials published from 1966 to February 2014.Interventions: patients were randomly assigned to a cardiac output–guided hemodynamic therapy algorithm for intravenous fluid and inotrope (dopexamine) infusion during and 6 hours following surgery (n=368) or to usual care (n=366).Main outcomes and measures: the primary outcome was a composite of predefined 30-day moderate or major complications and mortality. Secondary outcomes were morbidity on day 7; infection, critical care–free days, and all-cause mortality at 30 days; all-cause mortality at 180 days; and length of hospital stay.Results: baseline patient characteristics, clinical care, and volumes of intravenous fluid were similar between groups. Care was nonadherent to the allocated treatment for less than 10% of patients in each group. The primary outcome occurred in 36.6% of intervention and 43.4% of usual care participants (relative risk [RR], 0.84 [95% CI, 0.71-1.01]; absolute risk reduction, 6.8% [95% CI, ?0.3% to 13.9%]; P?=?.07). There was no significant difference between groups for any secondary outcomes. Five intervention patients (1.4%) experienced cardiovascular serious adverse events within 24 hours compared with none in the usual care group. Findings of the meta-analysis of 38 trials, including data from this study, suggest that the intervention is associated with fewer complications (intervention, 488/1548 [31.5%] vs control, 614/1476 [41.6%]; RR, 0.77 [95% CI, 0.71-0.83]) and a nonsignificant reduction in hospital, 28-day, or 30-day mortality (intervention, 159/3215 deaths [4.9%] vs control, 206/3160 deaths [6.5%]; RR, 0.82 [95% CI, 0.67-1.01]) and mortality at longest follow-up (intervention, 267/3215 deaths [8.3%] vs control, 327/3160 deaths [10.3%]; RR, 0.86 [95% CI, 0.74-1.00]).Conclusions and relevance: in a randomized trial of high-risk patients undergoing major gastrointestinal surgery, use of a cardiac output–guided hemodynamic therapy algorithm compared with usual care did not reduce a composite outcome of complications and 30-day mortality. However, inclusion of these data in an updated meta-analysis indicates that the intervention was associated with a reduction in complication rate

Technology readiness levels for machine learning systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Technology readiness levels for machine learning systems

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics

Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics