Predicting Aflatoxin Contamination in Crops Using Machine Learning Algorithms

Mathematical model to assess the impacts of aflatoxin contamination in crops, livestock and humans

Understanding the complex interrelationships of plant and fungal gene products during the host plant—Aspergillus flavus interaction is key in developing strategies to interrupt the aflatoxin contamination process. Fungal processes necessary for invasion of the plant and production of aflatoxin can be broken down into three categories: 1) cell wall degradation (cellulases, pectinases, amylases, and proteinases), 2) fungal development (cell wall synthesis and conidiophore and conidial formation), and 3) aflatoxin biosynthesis and regulation. Fungal expressed sequence tag (EST) projects have led to a rapid expansion in the number of candidate genes governing the processes involved in invasion of the plant and aflatoxin contamination. Plant factors have been discovered through the use of proteomics and natural product chemistry that may influence fungal processes involved in invasion and aflatoxin contamination. These factors can also be divided into three categories: 1) seed proteins/inhibitors of fungal cel...

Aflatoxin contamination of important food and feed crops occurs frequently in warm tropical and subtropical regions. The contamination is caused mainly by Aspergillus flavus and A. parasiticus. Aflatoxin contamination negatively affects health and trade sectors and causes economic losses to agricultural industries. Many pre- and post-harvest technologies can limit aflatoxin contamination but may not always reduce aflatoxin concentrations below tolerance thresholds. However, the use of atoxigenic (non-toxin producing) isolates of A. flavus to competitively displace aflatoxin producers is a practical strategy that effectively limits aflatoxin contamination in crops from field to plate. Biocontrol products formulated with atoxigenic isolates as active ingredients have been registered for use in the US, several African nations, and one such product is in final stages of registration in Italy. Many other nations are seeking to develop biocontrol products to protect their crops. In this review article we present an overview of the biocontrol technology, explain the basis to select atoxigenic isolates as active ingredients, describe how formulations are developed and tested, and describe how a biocontrol product is used commercially. Future perspectives on formulations of aflatoxin biocontrol products, along with other important topics related to the aflatoxin biocontrol technology are also discussed.

Aspergillus flavus is a diverse assemblage of strains that include aflatoxin-producing and non-toxigenic strains with cosmopolitan distribution. The most promising strategy currently being used to reduce preharvest contamination of crops with aflatoxin is to introduce non-aflatoxin (biocontrol) A. flavus into the crop environment. Whether or not introduction of biocontrol strains into agricultural fields is enough to reduce aflatoxin contamination to levels required for acceptance of the contaminated food as fit for consumption is still unknown. There is no question that biocontrol strains are able to reduce the size of the populations of aflatoxin-producing strains but the available data suggests that at most only a four- to five-fold reduction in aflatoxin contamination is achieved. There are many challenges facing this strategy that are both short term and long term. First, the population biology of A. flavus is not well understood due in part to A. flavus’s diversity, its ability to form heterokaryotic reproductive forms, and its unknown ability to survive for prolonged periods after application. Second, biocontrol strains must be selected that are suitable for the environment, the type of crop, and the soil into which they will be introduced. Third, there is a need to guard against inadvertent introduction of A. flavus strains that could impose an additional burden on food safety and food quality, and fourth, with global warming and resultant changes in the soil nutrients and concomitant microbiome populations, the biocontrol strategy must be sufficiently flexible to adapt to such changes. Understanding genetic variation within strains of A. flavus is important for developing a robust biocontrol strategy and it is unlikely that a “one size fits all” strategy will work for preharvest aflatoxin reduction.

Isolation and evaluation of probiotics from traditional Chinese foods for aflatoxin B1 detoxification: Geotrichum candidum XG1 (yeast) and mechanistic insights

Sexual reproduction and recombination in the aflatoxin-producing fungus Aspergillus parasiticus

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model–agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

Aflatoxin contamination caused by Aspergillus flavus infection of corn is a significant and chronic threat to corn being used as food or feed. Contamination of crops at levels of 20 ng g−1 or higher (as regulated by the USFDA) by this toxin and potent carcinogen makes the crop unsalable, resulting in a significant economic burden on the producer. This review focuses on elimination of this contamination in corn which is a major US crop and the basis of many products. Corn is also “nature's example” of a crop containing heritable resistance to aflatoxin contamination, thereby serving as a model for achieving resistance to aflatoxin contamination in other crops as well. This crop is the largest production grain crop worldwide, providing food for billions of people and livestock and critical feedstock for production of biofuels. In 2011, the economic value of the US corn crop was US$76 billion, with US growers producing an estimated 12 billion bushels, more than one-third of the world's supply. Thus, the economics and significance of corn as a food crop and the threat to food safety due to aflatoxin contamination of this major food crop have prompted the many research efforts in many parts of the world to identify resistance in corn to aflatoxin contamination. Plant breeding and varietal selection has been used as a tool to develop varieties resistance to disease. This methodology has been employed in defining a few corn lines that show resistance to A. flavus invasion; however, no commercial lines have been marketed. With the new tools of proteomics and genomics, identification of resistance mechanisms, and rapid resistance marker selection methodologies, there is an increasing possibility of finding significant resistance in corn, and in understanding the mechanism of this resistance.

Aflatoxin contamination of crops is a world-wide problem. Lethal aflatoxicosis of humans has been associated with maize produced in Kenya for over three decades. The S strain morphotype of Aspergillus flavus was identified as the primary cause of aflatoxin contamination events occurring between 2004 and 2006 in Kenya. Because the S strain was first described in the U.S., it was suggested that the agent causing lethal levels of aflatoxins was introduced to Kenya with maize. DNA sequence comparisons among 68 S strain isolates from Kenya, the Americas, Asia, and Australia suggest the Kenyan isolates are distinct from those causing aflatoxin contaminations in the U.S. Analyses of 4.06 kb representing three loci from distinct chromosomes indicate that most S strain isolates from the U.S. resolved into a clade distinct from one containing the 30 Kenyan isolates. S strain isolates from Kenya were more closely related to the recently described species A. minisclerotigenes than to A. flavus. Furthermore, failure of the Kenyan isolates to produce G aflatoxins was attributed to a previously undescribed deletion in the cypA gene, suggesting that different deletion events led to loss of G aflatoxin production in S strain isolates from the U.S. and Kenya. Thus, although the Kenyan isolates have S strain morphology and produce large quantities of only B aflatoxins like A. flavus S strain isolates, these isolates are phylogenetically divergent from those described from other regions. The molecular characteristics of the Kenyan S strain isolates described herein are valuable tools to identify and track these highly aflatoxigenic fungi.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

The integration of machine learning (ML) algorithms into audit processes represents a significant advancement in the field of auditing, offering substantial benefits in terms of efficiency, accuracy, and risk management. This review examines the transformative potential of ML in auditing, highlighting its key benefits and the challenges that must be addressed to fully leverage its capabilities. Machine learning algorithms, with their ability to analyze large datasets and identify patterns, enhance the accuracy and thoroughness of audits. Traditional auditing methods often rely on sampling and manual checks, which can miss anomalies and fraudulent activities. In contrast, ML algorithms can process entire datasets, uncovering subtle patterns and irregularities that may indicate fraud or errors. This comprehensive analysis reduces the risk of oversight and improves the reliability of audit findings. One of the primary benefits of ML in auditing is its capacity for anomaly detection. ML models can be trained on historical data to understand normal financial behavior and flag deviations that might signify irregularities. This ability to detect anomalies in real-time enables auditors to identify potential issues promptly, reducing the time lag between occurrence and detection of fraud. Predictive analytics, powered by ML, further enhances audit processes by forecasting future risks based on historical data. This proactive approach allows auditors to anticipate and mitigate risks before they materialize, contributing to more robust risk management strategies. Despite these advantages, integrating ML into audit processes presents several challenges. Ensuring data quality and integrity is crucial, as ML algorithms are only as good as the data they analyze. Poor-quality data can lead to inaccurate predictions and conclusions. Additionally, the "black box" nature of some ML algorithms can pose transparency issues, making it difficult for auditors to explain how specific conclusions were reached, which is critical for stakeholder trust and regulatory compliance. Another significant challenge is the potential for algorithmic bias. ML models can inadvertently perpetuate existing biases in the data, leading to unfair or skewed audit outcomes. Continuous monitoring and validation of ML algorithms are necessary to detect and mitigate such biases. In conclusion, while integrating machine learning algorithms into audit processes offers substantial benefits in terms of accuracy, efficiency, and risk management, it also necessitates careful attention to data quality, transparency, and bias mitigation. Addressing these challenges is essential to fully realize the potential of ML in enhancing audit practices. Keywords: Benefits, Challenges, Audit Processes, Algorithms, ML.

Machine learning (ML) algorithms seek to extract the most beneficial information out of the raw data. As mentioned in previous chapters, preprocess data preparation actions might be needed in order to make the data set ready to be fed into the ML algorithms. ML algorithms have gotten the attention of researchers all over the world during the last few decades. In traditional algorithms, the analyst had to define the rules, which was not always accessible or easily possible, in order to obtain the output. ML algorithms develop the models or rules based on the training data set which include input and output data points. ML algorithms try to understand more beneficial information regarding the system based on the training data set. The built model can be tested using the verification data set which does not have any overlap with the training sets. If the model acquires the acceptable performance measures, it can be applied for other cases to perform the prediction process. It should be considered that ML algorithms are not supposed to do the magic. Indeed, they are supposed to explore and analyze the training data, which the human brain is not able to handle that, to develop a predictive model in which its performance is acceptable for the verification data set. Predictive models should be generalized which means performing satisfactorily for both training and verifying data sets.

A machine learning (ML) algorithm for automatic segmentation of intravascular ultrasound was previously validated. It has the potential to improve efficiency, accuracy and precision of coronary vessel segmentation compared to manual segmentation by interventional cardiology experts. The aim of this study is to compare the performance of human readers to the machine and against the readings from a Core Laboratory. This is a post-hoc, cross-sectional analysis of the IBIS-4 study. Forty frames were randomly selected and analyzed by 10 readers of varying expertise two separate times, 1week apart. Their measurements of lumen, vessel, plaque areas, and plaque burden were performed in an offline software. Among humans, the intra-observer variability was not statistically significant. For the total 80 frames, inter-observer variability between human readers, the ML algorithm and Core Laboratory for lumen area, vessel area, plaque area and plaque burden were not statistically different. For lumen area, however, relative differences between the human readers and the Core Lab ranged from 0.26 to 12.61%. For vessel area, they ranged from 1.25 to 9.54%. Efficiency between the ML algorithm and the readers differed notably. Humans spent 47min on average to complete the analyses, while the ML algorithm took on average less than 1min. The overall lumen, vessel and plaque means analyzed by humans and the proposed ML algorithm are similar to those of the Core Lab. Machines, however, are more time efficient. It is warranted to consider use of the ML algorithm in clinical practice.

Wind energy has the possibility for bringing out energy in a very constant and sustainable manner being a notable and eligible source. Although, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and therefore, the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which will also aid in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day to day wind speed info using machine learning and deep learning algorithms. A way is put forward by us to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term these algorithms may be used for wind generation rate with historical relation to wind speed info. Index Terms: Wind turbine,machine learning algorithm