1,720,955 research outputs found
Risk assessment of aflatoxin in Iowa corn post-harvest using an event tree analysis: A case study
Get PDFMycotoxins are secondary metabolites produced by fungi found in corn and are anticipated to increase globally due to enhanced weather extremes and climate change. Aflatoxin (AFL) is of concern due to its harmful effects on human and animal health. AFL can move through complex grain supply chains in the United States, including multiple stakeholders from farms, grain elevators, grain and ethanol processors, and feed mills, before reaching end users, putting numerous entities at risk. Since corn is an essential food and feed product, risk management of AFL must be considered. This case study aimed to (1) calculate the probabilities of pivotal events with AFL in corn at Food Safety Modernization Act-regulated entities using an event tree analysis (ETA) and (2) propose recommendations based on factors identified through the ETA for AFL risk management. The ETA was based on historical AFL prevalence data in Iowa above a 20-part per billion (ppb) threshold (2.30%). Results showed four single-point failures in feed safety systems, where countermeasures did not function as designed. Failure is defined as the type 2 error of corn being infected with AFL 20 ppb, and the overall system fails to detect this with contaminated corn reaching end users. The success rate is defined as detecting the corn samples correctly >20 ppb. The average success rate was 50.14%, and the failure rate was 49.86%. It was concluded that risk-informed decisions are a critical component of effective AFL monitoring in corn, with timely intervention strategies needed to minimize the overall effects on end users.This article is published as Branstad‐Spates, Emily, Gretchen A. Mosher, and Erin Bowers. "Risk assessment of aflatoxin in Iowa corn post‐harvest using an event tree analysis: A case study." Risk Analysis (2024). doi: https://doi.org/10.1111/risa.15074. © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/)
Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year
Get PDFWarm temperatures and drought conditions in the United States (US) Corn Belt in 2012 raised concern for widespread aflatoxin (AFL) contamination in Iowa corn. To identify the prevalence of AFL in the 2012 corn crop, the Iowa Department of Agriculture and Land Stewardship (IDALS) conducted a sample of Iowa corn to assess the incidence and severity of AFL contamination. Samples were obtained from grain elevators in all of Iowa’s 99 counties, representing nine crop reporting districts (CRD), and 396 samples were analyzed by IDALS using rapid test methods. The statewide mean for AFL in parts per billion (ppb) was 5.57 ppb. Regions of Iowa differed in their incidence levels, with AFL levels significantly higher in the Southwest (SW; mean 15.13 ppb) and South Central (SC; mean 10.86 ppb) CRD () regions of Iowa. This sampling demonstrated high variability among samples collected within CRD and across the entire state of Iowa in an extreme weather event year. In years when Iowa has AFL contamination in corn, there is a need for a proactive and preventive strategy to minimize hazards in domestic and export markets.This article is published as Branstad-Spates, Emily H., Erin L. Bowers, Charles R. Hurburgh, Philip M. Dixon, and Gretchen A. Mosher. "Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year." International Journal of Food Science 2023 (2023). doi: https://doi.org/10.1155/2023/9959998. Copyright © 2023 Emily H. Branstad-Spates et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Predicting fumonisins in Iowa corn: Gradient boosting machine learning
Get PDFBackground and Objectives: Fumonisin (FUM), a secondary metabolite from Fusarium spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois-centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa-centric model. Corn samples (n = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (n = 89). Findings: Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois- and Iowa-centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois- and Iowa-centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%. Conclusions
FUM-GBM analyses determined the top influential predictor for the Illinois-centric model was satellite-acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa-centric model was precipitation (PRCP) in October. Significance and Novelty: Results indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.This article is published as Branstad‐Spates, Emily, Lina Castano‐Duque, Gretchen Mosher, Charles Hurburgh Jr, Kanniah Rajasekaran, Phillip Owens, H. Edwin Winzeler, and Erin Bowers. "Predicting fumonisins in Iowa corn: Gradient boosting machine learning." Cereal Chemistry (2024). doi: https://doi.org/10.1002/cche.10824. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted
Integrated mycotoxin risk management strategies for grain handling and feed manufacturing industries
Get PDFMajor advancements have been made regarding mycotoxin management in food and feed products; this can largely be attributed to improvements in knowledge of how mycotoxins develop from fungi under certain environmental conditions, incorporating advanced technology, and the great importance of utilizing integrated mycotoxin management strategies as a holistic approach. Mycotoxin management is dire, as many associated challenges include economic losses, reduced crop yields and value, detrimental effects on livestock production parameters, and human health impacts. This research explored integrated mycotoxin risk management strategies in grain handling and feed manufacturing industries in the United States (US) with a particular focus on aflatoxins (AFL). No matter how adequate the risk management strategy is for AFL, it must be implemented by stakeholders in the integrated supply chain. Therefore, stakeholders’ perceptions of mycotoxin risk influence the degree to which they may properly interpret and implement a management strategy. Improved predictive models can improve stakeholder confidence in their investments in mycotoxin management and their decision-making. The first study in this research aimed to identify risk perceptions associated with mycotoxin management in grain handling and feed manufacturing facilities in the Midwest region of the US among three stakeholder groups. Qualitative, semi-structured interviews and a thematic analysis identified six major themes: 1). challenges throughout the integrated grain and feed supply chain, 2). communication, 3). future research, 4). mitigation strategies, 5). mycotoxin occurrences, and 6). quality and food safety management. Furthermore, differences were observed among stakeholder groups with regard to their perception of mycotoxins as a hazard on a risk assessment scale and mycotoxin predictions in the future. These findings contribute to a broader effort to understand relevant supply chain stakeholder perceptions of mycotoxins and to provide information on how to better inform risk management strategies, tying into the next objective of understanding influential factors for AFL contamination in Iowa corn. In 2012, hot drought conditions in the US Corn Belt raised concerns about widespread AFL contamination. The Iowa Department of Agriculture and Land Stewardship (IDALS) sampled Iowa corn to assess the state's incidence and severity of AFL contamination. Three hundred ninety-six samples were analyzed for AFL; the statewide average mean for all tested samples was 5.57 ppb. Compared with the rest of the state, AFL levels were significantly higher in the Southwest (SW; mean 15.13 ppb) and South Central (SC; mean 10.86 ppb) crop reporting districts (CRD). Using the case study as a guideline, the next objective aimed to evaluate the performance of AFL prediction in Iowa corn utilizing gradient boosting machine (GBM) learning and feature engineering. Historical monthly meteorological and soil property data from four years, combined with historical Iowa AFL data collected in the same four years (2010, 2011, 2012, and 2021), were used in the GBM model for two AFL risk thresholds for high contamination events: 20-ppb and 5-ppb. An overall accuracy of 96.77% was achieved for AFL prediction with the GBM model, with a balanced accuracy of 50.00% for a 20-ppb risk threshold, whereas an overall accuracy of 90.32% with a balanced accuracy of 64.88% was developed for a 5-ppb threshold. Mycotoxin prediction models are practical and implementable in commodity grain handling environments and can aid in achieving the goal of being preventative versus reactive to outbreaks. Therefore, the last study aimed to 1). Understand the probabilities of pivotal events with AFL in corn at FSMA-regulated entities in food and feed safety systems through an event tree analysis (ETA), and 2). Propose recommendations based on factors identified through the ETA and Iowa-centric model for AFL risk management. The ETA was used to systematically evaluate pivotal events and hypothetical safety-decision-making scenarios for corn users based on the AFL risk each year calculated by the Iowa-centric model. The results showed four single-point failures (SPF) within the food and feed safety systems for AFL control, with an overall failure of 49.86% and 50.14% success rate, respectively. This research concluded that risk-informed decisions are needed for effective AFL monitoring and prediction in Iowa corn with timely intervention strategies to minimize the overall effects on end-users
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
