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62 results for “aflatoxin”
The diversity and function of the peanut pods-associated microbiota and their effects on aflatoxin contamination in China
<p>In essence, these results are instructive for developing novel aflatoxin-control technology, selecting for better peanut species, and predicting aflatoxin contamination, therefore are of great interest for improving peanut quality and safety. </p>
Moisture content and total aflatoxin content of the freshly harvested maize samples
<p>Moisture content and total aflatoxin content of the freshly harvested maize samples. </p> <p>Moisture content of the samples were determined on-site in triplicate using Superpoint handheld moisture analyzer (Supertech Agroline, Hestchaven 5, DK-5400 Bogense, Denmark; ±0.5% accuracy) following the manufacturer’s instructions.</p> <p>Total AF in the samples were quantified by a single step lateral flow immunoassay utilizing the developed Reveal Q+ test strip for Aflatoxin (Neogen Item 8085) read on a calibrated AccuSan Gold reader (Neogen Corporation, 620 Lesher Place, Lansing, MI 48912 USA) (Neogen item 9595) at 18-22<sup>o</sup>C</p>
Efficacy of Low Temperature Nitrogen Plasma in Destroying Fungi and Aflatoxin in Maize - data set
<p>Data that was collected during optimization of the decontamination processes in maize using Response Surface Methodology</p>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Dataset: Aflatoxin B1 Metabolism of Reared Alphitobius diaperinus in Different Life-Stages
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Efficacy of low temperature plasma in eliminating fungi and aflatoxins - data set
<p>Set of data generated during the experimental runs</p>
Data from: Combining ability of experimental maize lines for yield and aflatoxin in the southeastern USA
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Annexes to the risk assessment of aflatoxins in food
<p>The annexes A to E to the Scientific Opinion on Aflatoxins in Food included in the upload are excel files as follows:</p> <ul> <li>Annex A: Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment</li> <li>Annex B: Occurrence data on aflatoxins </li> <li>Annex C: Proportion of left-censored data and the mean concentrations of the quantified analytical results of AFB1 for pistachios, hazelnuts, peanuts, other nuts and dried figs</li> <li>Annex D: AFB1 and AFM1 concentrations reported for organic farming and conventional farming in selected food categories </li> <li>Annex E: Mean and high chronic dietary exposure to aflatoxins per survey and the contribution of different food groups to the dietary exposure </li> </ul> <p> </p>
Evaluation of ACCS100 to Reduce Aflatoxin Exposure in Kenya
ClinicalTrials.gov study NCT02188953. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Probiotics and Its Associated Factors on Aflatoxin Biomarkers
ClinicalTrials.gov study NCT03882294. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Balancing selection for aflatoxin in Aspergillus flavus is maintained through interference competition with, and fungivory by insects
The role of microbial secondary metabolites in the ecology of the organisms that produce them remains poorly understood. Variation in aflatoxin production by Aspergillus flavus is maintained by balancing selection, but the ecological function and impact on fungal fitness of this compound are unknown. We hypothesize that balancing selection for aflatoxin production in A. flavus is driven by interaction with insects. To test this, we competed naturally occurring aflatoxigenic and non-aflatoxigenic fungal isolates against Drosophila larvae on medium containing 0–1750 ppb aflatoxin, using quantitative PCR to quantify A. flavus DNA as a proxy for fungal fitness. The addition of aflatoxin across this range resulted in a 26-fold increase in fungal fitness. With no added toxin, aflatoxigenic isolates caused higher mortality of Drosophila larvae and had slightly higher fitness than non-aflatoxigenic isolates. Additionally, aflatoxin production increased an average of 1.5-fold in the presence of a single larva and nearly threefold when the fungus was mechanically damaged. We argue that the role of aflatoxin in protection from fungivory is inextricably linked to its role in interference competition. Our results, to our knowledge, provide the first clear evidence of a fitness advantage conferred to A. flavus by aflatoxin when interacting with insects.
Kisumu aflatoxin study - student Lilly Smith
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Evaluation of the Role of Aflatoxin as an Environmental Risk Factor Attributable to Liver Cancer in Nile Delta
ClinicalTrials.gov study NCT02461966. IPD Sharing: NO. Countries: 1. Publications: 0.
Aflatoxin Birth Cohort Study Nepal (AflaCohort)
ClinicalTrials.gov study NCT03312049. IPD Sharing: NO. Countries: 0. Publications: 3.
Data from: Balancing selection for aflatoxin in Aspergillus flavus is maintained through interference competition with, and fungivory by insects
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Genes Differentially Expressed by Aspergillus flavus Strains After Loss of Aflatoxin Production by Serial Transfers
GEO Series GSE8185. Aspergillus flavus. 12 samples. Type: Expression profiling by array.
Liver effects of Aflatoxin B1 (AFB1) in wild type (C57BL/6J) and hepatitis C virus-transgenic (HCV-Tg) mice
GEO Series GSE26838. Mus musculus. 20 samples. Type: Expression profiling by array.
SntB triggers the antioxidant pathways to regulate development and aflatoxin biosynthesis in Aspergillus flavus
GEO Series GSE247683. Aspergillus flavus. 14 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Aflatoxin B1 exposure induces epigenetic mechanisms in primary human hepatocytes revealing novel biological processes associated with hepatocellular carcinoma (MeDIP)
GEO Series GSE67005. Homo sapiens. 9 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Effect of Streptomyces roseolus cell free supernatant on fungal development, transcriptome and aflatoxin B1 production of Aspergillus flavus
GEO Series GSE232607. Aspergillus flavus; Penicillium expansum. 8 samples. Type: Expression profiling by array.
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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