Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.
more »
« less
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
-
more » « less
Abstract Background The development of public health policy is inextricably linked with governance structure. In our increasingly globalized world, human migration and infectious diseases often span multiple administrative jurisdictions that might have different systems of government and divergent management objectives. However, few studies have considered how the allocation of regulatory authority among jurisdictions can affect disease management outcomes.
Methods Here we evaluate the relative merits of decentralized and centralized management by developing and numerically analyzing a two-jurisdiction
SIRS model that explicitly incorporates migration. In our model, managers choose between vaccination, isolation, medication, border closure, and a travel ban on infected individuals while aiming to minimize either the number of cases or the number of deaths.Results We consider a variety of scenarios and show how optimal strategies differ for decentralized and centralized management levels. We demonstrate that policies formed in the best interest of individual jurisdictions may not achieve global objectives, and identify situations where locally applied interventions can lead to an overall increase in the numbers of cases and deaths.
Conclusions Our approach underscores the importance of tailoring disease management plans to existing regulatory structures as part of an evidence-based decision framework. Most importantly, we demonstrate that there needs to be a greater consideration of the degree to which governance structure impacts disease outcomes.
-
more » « less
Abstract Managing social‐ecological systems (SES) requires balancing the need to tailor actions to local heterogeneity and the need to work over large areas to accommodate the extent of SES. This balance is particularly challenging for policy since the level of government where the policy is being developed determines the extent and resolution of action.
We make the case for a new research agenda focused on ecological federalism that seeks to address this challenge by capitalizing on the flexibility afforded by a federalist system of governance. Ecological federalism synthesizes the environmental federalism literature from law and economics with relevant ecological and biological literature to address a fundamental question: What aspects of SES should be managed by federal governments and which should be allocated to decentralized state governments?
This new research agenda considers the bio‐geo‐physical processes that characterize state‐federal management tradeoffs for biodiversity conservation, resource management, infectious disease prevention, and invasive species control.
Read the free
Plain Language Summary for this article on the Journal blog. -
more » « less
Abstract The number of large‐scale, high‐severity forest fires occurring in the United States is increasing, as is the cost to suppress these fires. We use a technique developed by William Reed to incorporate the stochasticity of the time of a forest fire into our optimal control problem. Using this optimal control problem, we explore the trade‐offs between prevention management spending and suppression spending, along with the overall economic viability of prevention management spending. Our goal is to determine the optimal prevention management spending rate and the optimal suppression spending, which maximizes the expected value of a forest. We develop a parameter set reflecting the 2011 Las Conchas Fire and numerically solve our optimal control problem. Furthermore, we adapt this problem to simulate a sequence of fires and corresponding controls. Overall, our results support the conclusion that the prevention management efforts offset rising suppression costs and increase the value of a forest.
Recommendations for Resource Managers Increasing wildfire size and increasing federal suppression costs have prompted investigations into alternative methods to help prevent and manage large wildfires.
Fire prevention lowers the risk of experiencing large fire events, but investment in fire prevention is risky because its benefits are realized at an unknown time in the future.
Results illustrate that there are real economic costs associated with using funding directed to fire prevention to fund immediate fire suppression.
In our work with unknown fire sequences, we observe an 88% reduction in suppression spending on average with fire prevention, and a 55% reduction in spending overall.
