34 results
A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS)
- Jaclyn C. Kearns, Emily R. Edwards, Erin P. Finley, Joseph C. Geraci, Sarah M. Gildea, Marianne Goodman, Irving Hwang, Chris J. Kennedy, Andrew J. King, Alex Luedtke, Brian P. Marx, Maria V. Petukhova, Nancy A. Sampson, Richard W. Seim, Ian H. Stanley, Murray B. Stein, Robert J. Ursano, Ronald C. Kessler
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- Journal:
- Psychological Medicine / Volume 53 / Issue 15 / November 2023
- Published online by Cambridge University Press:
- 09 March 2023, pp. 7096-7105
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Background
Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
MethodsWe revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
ResultsTwelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
ConclusionsAn accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Associations of vulnerability to stressful life events with suicide attempts after active duty among high-risk soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-longitudinal study (STARRS-LS)
- Carol Chu, Ian H. Stanley, Brian P. Marx, Andrew J. King, Dawne Vogt, Sarah M. Gildea, Irving H. Hwang, Nancy A. Sampson, Robert O'Brien, Murray B. Stein, Robert J. Ursano, Ronald C. Kessler
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- Journal:
- Psychological Medicine / Volume 53 / Issue 9 / July 2023
- Published online by Cambridge University Press:
- 27 May 2022, pp. 4181-4191
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Background
The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking.
MethodsParticipants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs.
ResultsThe 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs.
ConclusionsIt might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.
A regional initiative to improve cleaning of high-touch surfaces in long-term care facilities
- David P. Calfee, Robert P. O’Neil, Quin Sylvester, Jared M. Bosk, Zeynep Sumer King, Karyn Langguth, Emily C. Lutterloh, Debra Blog
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue 7 / July 2020
- Published online by Cambridge University Press:
- 14 April 2020, pp. 844-847
- Print publication:
- July 2020
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A total of 38 long-term care facilities within a region participated in a 3-month quality improvement initiative focused on environmental cleaning and disinfection. Significant improvements in daily and discharge cleaning were observed during the project period. Further study of the sustainability and clinical impact of this type of initiative is warranted.
Refining and implementing the Food Assortment Scoring Tool (FAST) in food pantries
- Caitlin E Caspi, Katherine Y Grannon, Qi Wang, Marilyn S Nanney, Robert P King
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- Journal:
- Public Health Nutrition / Volume 21 / Issue 14 / October 2018
- Published online by Cambridge University Press:
- 29 May 2018, pp. 2548-2557
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Objective
Hunger relief agencies have a limited capacity to monitor the nutritional quality of their food. Validated measures of food environments, such as the Healthy Eating Index-2010 (HEI-2010), are challenging to use due to their time intensity and requirement for precise nutrient information. A previous study used out-of-sample predictions to demonstrate that an alternative measure correlated well with the HEI-2010. The present study revised the Food Assortment Scoring Tool (FAST) to facilitate implementation and tested the tool’s performance in a real-world food pantry setting.
DesignWe developed a FAST measure with thirteen scored categories and thirty-one sub-categories. FAST scores were generated by sorting and weighing foods in categories, multiplying each category’s weight share by a healthfulness parameter and summing the categories (range 0–100). FAST was implemented by recording all food products moved over five days. Researchers collected FAST and HEI-2010 scores for food availability and foods selected by clients, to calculate correlations.
SettingFive food pantries in greater Minneapolis/St. Paul, Minnesota, USA.
SubjectsFood carts of sixty food pantry clients.
ResultsThe thirteen-category FAST correlated well with the HEI-2010 in prediction models (r = 0·68). FAST scores averaged 61·5 for food products moved, 63·8 for availability and 62·5 for client carts. As implemented in the real world, FAST demonstrated good correlation with the HEI-2010 (r = 0·66).
ConclusionsThe FAST is a flexible, valid tool to monitor the nutritional quality of food in pantries. Future studies are needed to test its use in monitoring improvements in food pantry nutritional quality over time.
Multi-Year Validation of a Decision Aid for Integrated Weed Management in Row Crops
- Frank Forcella, Robert P. King, Scott M. Swinton, Douglas D. Buhler, Jeffrey L. Gunsolus
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- Journal:
- Weed Science / Volume 44 / Issue 3 / September 1996
- Published online by Cambridge University Press:
- 12 June 2017, pp. 650-661
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WEEDSIM is a bioeconomic decision aid for management of annual weeds in corn and soybean. It was field-tested for 4 yr in Minnesota. The decision aid has two categories of management recommendations: soil-applied plus postemergence (PRE+), based on estimated weed seedbank composition and density; and postemergence (POST), based upon observed weed seedling composition and density. Weed densities, weed control, herbicide use, environmental impact of herbicide use, weed management costs, crop yields, and economic returns that resulted from PRE+ and POST recommendations were compared to those associated with herbicide management systems (HERB) that were standard for the region. After 4 yr of applying WEEDSIM recommendations to the same plots, there were no increases in annual weed densities (seedbanks, seedlings, established plants, or seed production) or decreases in weed control or crop (soybean, rotation corn, and continuous corn) yields, compared to HERB. WEEDSIM recommendations resulted in average annual herbicide applications of 1.1 kg ai ha−1 for PRE+ and 1.0 kg ai ha−1 for POST, compared to 3.5 kg ai ha−1 for HERB. Environmental impact indices associated with PRE+, POST, and HERB were 0.75, 0.71, and 0.54, with the lowest value indicating greater environmental risk than the two higher values. Similarly, average weed management costs were $24, $33, and $77 ha−1 for PRE+, POST, and HERB, respectively. Based on crop prices of $94 Mg−1 for corn and $220 Mg-1 for soybean, the average gross margins over weed control costs were higher for PRE+ ($509 ha−1) and POST ($522 ha−1) than for HERB ($455 ha−1). In general, WEEDSIM appeared to make management recommendations that adequately controlled weeds, maintained crop yields, reduced herbicide use, decreased environmental risk, lowered weed management costs, and increased gross margins over weed control costs compared to the use of herbicides standard for the region.
Economic Analysis of Two Weed Management Systems for Two Cropping Rotations
- Donald W. Lybecker, Robert P. King, Edward E. Schweizer, Robert L. Zimdahl
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- Journal:
- Weed Science / Volume 32 / Issue 1 / January 1984
- Published online by Cambridge University Press:
- 12 June 2017, pp. 90-95
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A standard weed management system (system I) had a higher return above variable costs than did an intensive weed management system (system II) for two eastern Colorado cropping rotations. For continuous corn (Zea mays L.), the return above variable costs averaged $18.85/ha more under system I than under system II. For a barley (Hordeum vulgare L.)-corn-sugarbeet (Beta vulgaris L.) rotation, the return above variable costs averaged $20.48/ha more under System I than under System II. Based on alternative input (herbicide) and product prices, higher herbicide costs favored the standard weed management system, whereas higher crop prices favored the weed management system with the higher yields adjusted for quality. The probability that returns above variable costs differed between the two weed management systems depended upon the level of product prices and herbicide costs.
Field Evaluation of a Bioeconomic Model for Weed Management in Corn (Zea mays)
- Douglas D. Buhler, Robert P. King, Scott M. Swinton, Jeffery L. Gunsolus, Frank Forcella
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- Journal:
- Weed Science / Volume 44 / Issue 4 / December 1996
- Published online by Cambridge University Press:
- 12 June 2017, pp. 915-923
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A bioeconomic weed management model was tested as a decision aid for weed control in corn at Rosemount, MN, from 1991 to 1994. The model makes recommendations for preemergence control tactics based on the weed seed content of the soil and postemergence decisions based on weed seedling densities. Weed control, corn yield, herbicide active ingredient applied, and economic return with model-generated treatments were compared to standard herbicide and mechanical control treatments. Effects of these treatments on weed populations and soybean yield the following year were also determined. In most cases, the model-generated treatments controlled weeds as well as the standard herbicide treatment. The quantity of herbicide active ingredient applied decreased 27% with the seed bank model and 68% with the seedling model relative to the standard herbicide treatment. However, the frequency of herbicide application was not reduced. In 1 yr, seed bank model treatments did not control weeds as well as the standard herbicide or seedling model treatments. Corn yields reflected differences in weed control. Net economic return to weed control was not increased by using model-generated control recommendations. Weed control treatments the previous year affected weed density in the following soybean crop. In 2 of 3 yr, these differences did not after weed control or soybean yield. Although tactics differed, the bioeconomic model generally resulted in weed control and corn yield similar to the standard herbicide. The model was responsive to differing weed populations, but did not greatly after economic returns under the weed species and densities in this research.
Estimation of Crop Yield Loss Due to Interference by Multiple Weed Species
- Scott M. Swinton, Douglas D. Buhler, Frank Forcella, Jeffrey L. Gunsolus, Robert P. King
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- Journal:
- Weed Science / Volume 42 / Issue 1 / March 1994
- Published online by Cambridge University Press:
- 12 June 2017, pp. 103-109
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Previous efforts to model crop yield loss from multiple weed species constructed competitive indices based on yield loss from individual weed species. Our model uses a multispecies modification of Cousens’ rectangular hyperbolic yield function to estimate a nonlinear competitive index for weed-crop interference. Results from 13 Minnesota and Wisconsin data sets provide measures of the relative competitiveness of mixed green and yellow foxtails, common lambsquarters, redroot pigweed, velvetleaf, and several other weed species. Competition coefficient estimates are stable over years, but not locations.
Bioeconomic Modeling to Simulate Weed Control Strategies for Continuous Corn (Zea mays)
- Robert P. King, Donald W. Lybecker, Edward E. Schweizer, Robert L. Zimdahl
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- Journal:
- Weed Science / Volume 34 / Issue 6 / November 1986
- Published online by Cambridge University Press:
- 12 June 2017, pp. 972-979
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Grass and broadleaf weed densities and seed numbers, weed control practices, and grain yields were included in a bioeconomic model that evaluates alternative weed management strategies for continuous corn (Zea mays L.). Weed seed numbers in soil and herbicide carry-over provided intertemporal links. Four weed management strategies – two fixed, one mixed, and one flexible – were evaluated with annualized net returns as the performance indicator. The flexible strategy (weed control based on observed conditions) had the largest annualized net return for high and low initial weed seed numbers. The fixed weed management strategy (weed control predetermined) of an annual application of only a preemergence herbicide ranked second in terms of annualized net returns for high weed seed numbers. The mixed weed management strategy of alternative year applications of preemergence herbicide and “as needed” applications of postemergence herbicide ranked second for low initial weed seed numbers. The fixed weed management strategy of alternate year application of preemergence herbicide only generated the lowest annualized net return, regardless of initial weed seed numbers.
Weed Management Decisions in Corn Based on Bioeconomic Modeling
- Donald W. Lybecker, Edward E. Schweizer, Robert P. King
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- Journal:
- Weed Science / Volume 39 / Issue 1 / March 1991
- Published online by Cambridge University Press:
- 12 June 2017, pp. 124-129
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A fixed (conventional) weed management strategy in corn was compared to three other strategies (two mixed and one flexible) in terms of weed control, grain yield, gross margin (gross income minus herbicide treatment costs), and herbicide use under furrow irrigation for four consecutive years. The fixed strategy prespecified preplanting, preemergence, postemergence, and layby herbicides. The flexible strategy herbicide treatments were specified by a computer bioeconomic model. Model decisions were based on weed seed in soil before planting, weed densities after corn emergence, herbicide costs, expected corn grain yield and selling price, and other parameters. The two mixed strategies were a combination of fixed and flexible strategies and designated either specified soil-applied herbicides (mixed/soil), or no soil-applied herbicide (mixed/no soil); postemergence treatments were determined by the model. Average corn grain yield was 10 280 kg ha–1 and gross income was 920 $ ha–1 and neither differed among strategies. Total weed density and gross margin were significantly higher for the mixed/no soil and flexible strategies compared to the mixed/soil and fixed strategies. Total weed density averaged 28 720, 28 100, 10 910, and 680 plants ha–1 for the mixed/no soil, flexible, mixed/soil, and fixed strategies, respectively. Annual gross margins for the four strategies averaged 885, 875, 845, and 810 $ ha–1, respectively. Herbicide use over the 4-yr period for these four strategies averaged 3.8, 5.3, 20.5, and 26.9 kg ha–1, respectively, and each value differed from the other. Thus, weeds can be managed in corn, gross margins increased, and herbicide use decreased by employing a bioeconomic weed-corn model to make weed management decisions.
Field evaluation of a bioeconomic model for weed management in soybean (Glycine max)
- Douglas D. Buhler, Robert P. King, Scott M. Swinton, Jeffery L. Gunsolus, Frank Forcella
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- Journal:
- Weed Science / Volume 45 / Issue 1 / February 1997
- Published online by Cambridge University Press:
- 12 June 2017, pp. 158-165
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A bioeconomic model was tested as a decision aid for weed control in soybean at Rosemount, MN, from 1991 to 1994. The model makes recommendations for preplant incorporated and preemergence control tactics based on the weed seed content of the soil and postemergence decisions based on weed seedling densities. Weed control, soybean yield, herbicide use, and economic return with model-generated treatments were compared to standard herbicide and mechanical control systems. Effects of these treatments on weed populations and corn yield the following year were also determined. In most cases, the model-generated treatments controlled weeds as well as a standard herbicide treatment. Averaged over the 3 yr, the quantity of herbicide active ingredient applied was decreased by 47% with the seedbank model and 93% with the seedling model compared with a standard soil-applied herbicide treatment. However, the frequency of herbicide application was not reduced. Soybean yields reflected differences in weed control and crop injury. Net economic return to weed control was increased 50% of the time using model-recommended treatments compared with a standard herbicide treatment. Weed control treatments the previous year affected weed density in the following corn crop but had little effect on weed control or corn yield. The bioeconomic model was responsive to differing weed populations, maintained weed control and soybean yield and often increased economic returns under the weed species and densities in this research.
Economic Analysis of Four Weed Management Systems
- Donald W. Lybecker, Edward E. Schweizer, Robert P. King
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- Journal:
- Weed Science / Volume 36 / Issue 6 / November 1988
- Published online by Cambridge University Press:
- 12 June 2017, pp. 846-849
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An economic analysis of four weed management systems employed on four crop sequences in a barley-corn-pinto bean-sugarbeet rotation in eastern Colorado was computed. Weeds were controlled in each crop with only conventional tillage or conventional tillage plus minimum levels of herbicides (systems 3 and 4), moderate levels of herbicides (system 1), or intensive levels of herbicides (system 2). Adjusted gross returns were higher for systems 3 and 4 where herbicide use was less/year and decreased over 4 yr than for systems 1 and 2 where herbicide use was higher/year and constant. When the four crop sequences were aggregated using yield and sucrose indices, the least herbicide-intensive weed management system had $440/ha/4 yr higher indexed adjusted gross return than the most herbicide-intensive weed management system. An income risk analysis showed that the herbicide-intensive weed management system was not risk efficient and that producers would select one of the other three less herbicide-intensive weed management systems depending upon their risk preferences.
Evaluating the Economic Risk of Herbicide-Based Weed Management Systems in Corn and Soybean Using Stochastic Dominance Testing
- Thomas R. Hoverstad, Gregg A. Johnson, Jeffrey L. Gunsolus, Robert P. King
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- Journal:
- Weed Technology / Volume 20 / Issue 2 / June 2006
- Published online by Cambridge University Press:
- 20 January 2017, pp. 422-429
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Herbicide evaluation trials are typically conducted with the objective of rating herbicide efficacy and assessing crop yield loss. There is little if any attempt to quantify the economic risk associated with each treatment. The objective of this research was to use second-degree stochastic dominance to evaluate the economic stability of corn and soybean weed management systems between two contrasting environments. Weed management systems were evaluated in small-plot replicated trials over a 3-yr time period at two locations in southern Minnesota. One location (Waseca) had a slightly cooler and wetter environment than the second location (Lamberton). The Waseca location also had higher weed density and greater weed species diversity. Adjusted returns from weed management were calculated for each system by measuring economic returns, as determined by deducting weed management costs from the product of crop price and grain yield. Stochastic dominance is a technique that considers the entire distribution of net returns from weed management and compares these cumulative distributions as a basis for analyzing risk. Climate, soils, and weed diversity dictated differences in risk efficiency and effectiveness of the various weed management systems evaluated between the Waseca and Lamberton sites. Stochastic dominance testing is a useful tool for understanding long-term risk across environments. Results can be used to develop effective long-term weed management systems that minimize risk while maximizing profit potential.
Risk-Efficiency Criteria for Evaluating Economics of Herbicide-Based Weed Management Systems in Corn
- Thomas R. Hoverstad, Jeffrey L. Gunsolus, Gregg A. Johnson, Robert P. King
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- Journal:
- Weed Technology / Volume 18 / Issue 3 / September 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 687-697
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Evaluation of economic outcome associated with a given weed management system is an important component in the decision-making process within crop production systems. The objective of this research was to investigate how risk-efficiency criteria could be used to improve herbicide-based weed management decision making, assuming different risk preferences among growers. Data were obtained from existing weed management trials in corn conducted at the University of Minnesota Southern Research and Outreach Center at Waseca. Weed control treatments represented a range of practices including one-pass soil-applied, one-pass postemergence, and sequential combinations of soil and postemergence herbicide application systems. Analysis of risk efficiency across 23 herbicide-based weed control treatments was determined with the mean variance and stochastic dominance techniques. We show how these techniques can result in different outcomes for the decision maker, depending on risk attitudes. For example, mean variance and stochastic dominance techniques are used to evaluate risk associated with one- vs. two-pass herbicide treatments with and without cultivation. Based on these analyses, it appears that a one-pass system is preferred by a risk-averse grower. However, we argue that this may not be the best option considering potential changes in weed emergence patterns, application timing concerns, etc. The techniques for economic analysis of weed control data outlined in this article will help growers match herbicide-based weed management systems to their own production philosophies based on economic risk.
Application of the Healthy Eating Index-2010 to the hunger relief system
- Marilyn S Nanney, Katherine Y Grannon, Colin Cureton, Courtney Hoolihan, Mark Janowiec, Qi Wang, Cael Warren, Robert P King
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- Journal:
- Public Health Nutrition / Volume 19 / Issue 16 / November 2016
- Published online by Cambridge University Press:
- 25 May 2016, pp. 2906-2914
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Objective
To demonstrate the feasibility of applying the Healthy Eating Index-2010 (HEI-2010) to the hunger relief setting, specifically by assessing the nutritional quality of foods ordered by food shelves (front-line food provider) from food banks (warehouse of foods).
DesignThis Healthy FOOD (Feedback On Ordering Decisions) observational study used electronic invoices detailing orders made by 269 food shelves in 2013 and analysed in 2015 from two large Minnesota, USA food banks to generate HEI-2010 scores. Initial development and processing procedures are described.
ResultsThe average total HEI-2010 score for the 269 food shelves was 62·7 out of 100 with a range from 28 to 82. Mean component scores for total protein foods, total vegetables, fatty acids, and seafood and plant proteins were the highest. Mean component score for whole grains was the lowest followed by dairy, total fruits, refined grains and sodium. Food shelves located in micropolitan areas and the largest food shelves had the highest HEI-2010 scores. Town/rural and smaller food shelves had the lowest scores. Monthly and seasonal differences in scores were detected. Limitations to this approach are identified.
ConclusionsCalculating HEI-2010 for food shelves using electronic invoice data is novel and feasible, albeit with limitations. HEI-2010 scores for 2013 identify room for improvement in nearly all food shelves, especially the smallest agencies. The utility of providing HEI-2010 scores to decision makers in the hunger relief setting is an issue requiring urgent study.
The Agricultural Risk Management Simulator Microcomputer Program
- Robert P. King, J. Roy Black, Fred J. Benson, Patti A. Pavkov
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- Journal:
- Journal of Agricultural and Applied Economics / Volume 20 / Issue 2 / December 1988
- Published online by Cambridge University Press:
- 28 April 2015, pp. 165-171
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The Agricultural Risk Management Simulator (ARMS) is a microcomputer program designed to help users evaluate strategies for managing yield and price risk in crop farming operations. Risk management strategies are defined by choices regarding crop mix, the purchase of multiple peril crop insurance, and the use of forward contracting. Probabilistic budgeting is used to determine the net cash flow probability distribution for each strategy considered. Flexibility with regard to both sources of probabilistic information and the form of yield and price probability distributions is a noteworthy feature of the program.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
- Edited by Robert Audi, University of Notre Dame, Indiana
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- The Cambridge Dictionary of Philosophy
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- 05 August 2015
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- 27 April 2015, pp ix-xxx
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Contributors
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- By Rony A. Adam, Gloria Bachmann, Nichole M. Barker, Randall B. Barnes, John Bennett, Inbar Ben-Shachar, Jonathan S. Berek, Sarah L. Berga, Monica W. Best, Eric J. Bieber, Frank M. Biro, Shan Biscette, Anita K. Blanchard, Candace Brown, Ronald T. Burkman, Joseph Buscema, John E. Buster, Michael Byas-Smith, Sandra Ann Carson, Judy C. Chang, Annie N. Y. Cheung, Mindy S. Christianson, Karishma Circelli, Daniel L. Clarke-Pearson, Larry J. Copeland, Bryan D. Cowan, Navneet Dhillon, Michael P. Diamond, Conception Diaz-Arrastia, Nicole M. Donnellan, Michael L. Eisenberg, Eric Eisenhauer, Sebastian Faro, J. Stuart Ferriss, Lisa C. Flowers, Susan J. Freeman, Leda Gattoc, Claudine Marie Gayle, Timothy M. Geiger, Jennifer S. Gell, Alan N. Gordon, Victoria L. Green, Jon K. Hathaway, Enrique Hernandez, S. Paige Hertweck, Randall S. Hines, Ira R. Horowitz, Fred M. Howard, William W. Hurd, Fidan Israfilbayli, Denise J. Jamieson, Carolyn R. Jaslow, Erika B. Johnston-MacAnanny, Rohna M. Kearney, Namita Khanna, Caroline C. King, Jeremy A. King, Ira J. Kodner, Tamara Kolev, Athena P. Kourtis, S. Robert Kovac, Ertug Kovanci, William H. Kutteh, Eduardo Lara-Torre, Pallavi Latthe, Herschel W. Lawson, Ronald L. Levine, Frank W. Ling, Larry I. Lipshultz, Steven D. McCarus, Robert McLellan, Shruti Malik, Suketu M. Mansuria, Mohamed K. Mehasseb, Pamela J. Murray, Saloney Nazeer, Farr R. Nezhat, Hextan Y. S. Ngan, Gina M. Northington, Peggy A. Norton, Ruth M. O'Regan, Kristiina Parviainen, Resad P. Pasic, Tanja Pejovic, K. Ulrich Petry, Nancy A. Phillips, Ashish Pradhan, Elizabeth E. Puscheck, Suneetha Rachaneni, Devon M. Ramaeker, David B. Redwine, Robert L. Reid, Carla P. Roberts, Walter Romano, Peter G. Rose, Robert L. Rosenfield, Shon P. Rowan, Mack T. Ruffin, Janice M. Rymer, Evis Sala, Ritu Salani, Joseph S. Sanfilippo, Mahmood I. Shafi, Roger P. Smith, Meredith L. Snook, Thomas E. Snyder, Mary D. Stephenson, Thomas G. Stovall, Richard L. Sweet, Philip M. Toozs-Hobson, Togas Tulandi, Elizabeth R. Unger, Denise S. Uyar, Marion S. Verp, Rahi Victory, Tamara J. Vokes, Michelle J. Washington, Katharine O'Connell White, Paul E. Wise, Frank M. Wittmaack, Miya P. Yamamoto, Christine Yu, Howard A. Zacur
- Edited by Eric J. Bieber, Joseph S. Sanfilippo, University of Pittsburgh, Ira R. Horowitz, Emory University, Atlanta, Mahmood I. Shafi
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- Clinical Gynecology
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- 05 April 2015
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- 23 April 2015, pp viii-xiv
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Recommendations for Nanomedicine Human Subjects Research Oversight: An Evolutionary Approach for an Emerging Field
- Leili Fatehi, Susan M. Wolf, Jeffrey McCullough, Ralph Hall, Frances Lawrenz, Jeffrey P. Kahn, Cortney Jones, Stephen A. Campbell, Rebecca S. Dresser, Arthur G. Erdman, Christy L. Haynes, Robert A. Hoerr, Linda F. Hogle, Moira A. Keane, George Khushf, Nancy M. P. King, Efrosini Kokkoli, Gary Marchant, Andrew D. Maynard, Martin Philbert, Gurumurthy Ramachandran, Ronald A. Siegel, Samuel Wickline
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- Journal of Law, Medicine & Ethics / Volume 40 / Issue 4 / Winter 2012
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- 01 January 2021, pp. 716-750
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- Winter 2012
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Nanomedicine is yielding new and improved treatments and diagnostics for a range of diseases and disorders. Nanomedicine applications incorporate materials and components with nanoscale dimensions (often defined as 1-100 nm, but sometimes defined to include dimensions up to 1000 nm, as discussed further below) where novel physiochemical properties emerge as a result of size-dependent phenomena and high surface-to-mass ratio. Nanotherapeutics and in vivo nanodiagnostics are a subset of nanomedicine products that enter the human body. These include drugs, biological products (biologics), implantable medical devices, and combination products that are designed to function in the body in ways unachievable at larger scales. Nanotherapeutics and in vivo nanodiagnostics incorporate materials that are engineered at the nanoscale to express novel properties that are medicinally useful. These nanomedicine applications can also contain nanomaterials that are biologically active, producing interactions that depend on biological triggers. Examples include nanoscale formulations of insoluble drugs to improve bioavailability and pharmacokinetics, drugs encapsulated in hollow nanoparticles with the ability to target and cross cellular and tissue membranes (including the bloodbrain barrier) and to release their payload at a specific time or location, imaging agents that demonstrate novel optical properties to aid in locating micrometastases, and antimicrobial and drug-eluting components or coatings of implantable medical devices such as stents.
Contributors
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- By Ashraf Abdelhay, Ulrich Ammon, Angelelli Claudia V, David F. Armstrong, Peter Backhaus, Richard B. Baldauf Jr, Carol Benson, Richard D. Brecht, Stephen J. Caldas, Jasone Cenoz, Mary Carol Combs, Florian Coulmas, Helder De Schutter, Fernand de Varennes, Alexandre Duchêne, John Edwards, Gibson Ferguson, Ofelia García, Durk Gorter, Federica Guerini, Monica Heller, Gabrielle Hogan-Brun, Björn H. Jernudd, Kendall A. King, Verena Krausneker, Joseph Lo Bianco, Busi Makoni, Makoni Sinfree B, Pedzisai Mashiri, A. W. Teresa L. McCarty, Svitlana Melnyk, Jiří Nekvapil, Hoa Thi Mai Nguyen, Christina Bratt Paulston, Susan D. Penfield, Robert Phillipson, Meital Pinto, Adam Rambow, Denise Réaume, William P. Rivers, David Robichaud, Julia Sallabank, Bernard Spolsky, Stephen L. Walter, Jonathan M. Watt, Sherman Wilcox, Colin H. Williams, Sue Wright
- Edited by Bernard Spolsky, Bar-Ilan University, Israel
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- The Cambridge Handbook of Language Policy
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- 05 June 2012
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- 01 March 2012, pp xii-xiv
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