Source: KANSAS STATE UNIV submitted to
FOOD ANIMAL RESIDUE AVOIDANCE DATABANK (FARAD)
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
OTHER GRANTS
Reporting Frequency
Annual
Accession No.
1027019
Grant No.
2021-41480-35267
Cumulative Award Amt.
$250,000.00
Proposal No.
2021-08053
Multistate No.
(N/A)
Project Start Date
Sep 1, 2021
Project End Date
Aug 31, 2024
Grant Year
2021
Program Code
[FARAD]- Food An. Res. Avoidance Database,FARAD
Project Director
Jaberi-Douraki, M.
Recipient Organization
KANSAS STATE UNIV
(N/A)
MANHATTAN,KS 66506
Performing Department
Olathe
Non Technical Summary
The FARAD program is a national food safety program funded for 38 years since 1982 by USDA. The program a collaborative effort with five regional centers: Kansas State University--Olathe (KSUO), North Carolina State University, University of California--Davis, University of Florida, and Virginia-Maryland College of Veterinary Medicine. The goal of FARAD is to provide the most updated information and scientific tools to help the production of safe foods of animal origin. The program accomplishes this goal through its objectives: (1) to identify, extract, assemble, evaluate and distribute reviewed information about residue avoidance and mitigation to people involved in residue avoidance programs; (2) to develop tools that allow people to predict proper withdrawal intervals after extralabel drug use. To continue to fulfill the mission of FARAD, during 2021-2022, the specific objectives at KSUO include: (1) to develop interfaces by which FARAD responders can access the KSUO comparative medicine databases and associated computational tools, as well as accessing relevant global data, (2) to pursue the goal of developing novel AI technologies practical to food-animal production for collecting, organizing, merging, and cleaning data and help easily submit queries to obtain information regarding the datasets of Global maximum residue limits (MRLs) and withdrawal periods (WDPs), and (3) to conduct research in designing a full-text retrieval system that will enable the creation of an information retrieval database that can be easily queried, performing metadata analysis on bibliographic records in veterinary medicine as well as those validated by the current FARAD database.
Animal Health Component
20%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
5023910208050%
5023910209050%
Knowledge Area
502 - New and Improved Food Products;

Subject Of Investigation
3910 - Cross-commodity research--multiple animal species;

Field Of Science
2090 - Statistics, econometrics, and biometrics; 2080 - Mathematics and computer sciences;
Goals / Objectives
Kansas State University-Olathe (KSUO)will be responsible for developing interfaces by which FARAD responders can access the KSUO comparative medicine databases and associated computational tools, as well as accessing relevant global data. The KSUO team will pursue the goal of developing novel AI technologies practical to food-animal production for collecting, organizing, merging, and cleaning data and help easily submit queries to obtain information regarding the datasets of Global maximum residue limits (MRLs) and withdrawal periods (WDPs). They will also conduct research in designing a full-text retrieval system that will enable the creation of an information retrieval database that can be easily queried, performing metadata analysis on bibliographic records in veterinary medicine as well as those validated by the current FARAD database. This information retrieval system is significant and will be used to develop hybrid models of content-based and bibliometric features for machine learning applications to extract and clean data from different resources as an integral part of the next-generation FARAD program.
Project Methods
Many students will be trained in data analytics techniques in the preparation and analysis of our database curation to make a decision and interpret our results. These will be trained to develop a working hypothesis and design error correction techniques for data scrubbing and retrieval. Subsequently, they will implement data exploration technique for initial data analysis to visually explore and understand the characteristics of the data, then data curation and annotation will be performed to organize and integrate data collected from various sources, this phase entails annotation, organization, clustering, and presentation of the assorted data types from the 1DATA databank. Next, integration of machine learning models will be developed to help acquire results after data preprocessing and cleansing that significantly reduces the size of data and eliminates insignificant and noise-driven reports, and eventually enhances decision and interpretation via data-driven machine learning. Specifically, statistical methods used for data analytics during this project include regression analysis, cohort and cluster analysis, feature reduction, and data visualization.

Progress 09/01/21 to 08/31/24

Outputs
Target Audience:We aim to engage a diverse group of stakeholders, including veterinarians, producers, researchers, and students, from undergraduates to graduate-level investigators. These students will benefit from ongoing mentorship and hands-on training throughout the project. To promote collaboration and the exchange of knowledge, we will facilitate weekly team meetings within the KUSO group and host quarterly gatherings with other FARAD units to present and discuss the latest research and scientific developments. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The estimated Full-Time Equivalent (FTE) breakdown is as follows: Two graduate students: 1.0 FTE Three postdoctoral researchers: 2.5FTE How have the results been disseminated to communities of interest?As outlined above, the primary methods for information dissemination for FARAD include the call center and internet access. In addition to these channels, we have published our research in peer-reviewed journals (refer to the Products page) and shared our findings at local, regional, and national meetings. Notably, our research was presented at the Annual Meeting of the Society of Toxicology in2024. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Major Goals of the Project Overal FARAD Program Goal: The primary objective of the national FARAD program is to protect public health by ensuring that animal-derived food products--such as milk, eggs, meat, and honey--are free from harmful chemical residues. This includes substances like drugs, pesticides, environmental contaminants, natural toxins, and other potentially unsafe chemicals. Six Specific Objectives of the National FARAD Program: Extract and validate data for integration into the system, supporting FARAD's drug databases, and publishing them electronically (VetGRAM) for internet-based access, including data from foreign drug compendia and gFARAD partners. Operate Regional Access Centers (RACs) at NCSU, VMCVM, and UCD, providing residue avoidance information through a toll-free hotline and online submission forms. Maintain FARAD files, including data entry, pharmacokinetic analysis, and distribution. Prepare FARAD Digests for publication in the Journal of the AVMA, newsletters, and web-based fact sheets/species information. Educate veterinarians on drug residue avoidance strategies. Continue the development and validation of extrapolative methods for advising in situations where direct data is unavailable, particularly for environmental contaminants. KSU Component Goal: The KSU component of the FARAD program focuses on creating web-based platforms and databases that enable FARAD responders to calculate real-time drug withdrawal intervals for extralabel drug use in various food animal species. Three Specific Objectives of the KSU Component: Continue the development of a Physiological Parameter Database for multiple food animal species, including goats, sheep, chickens, and turkeys. Expand FARAD databases to foster global connectivity. Developartificial intelligence systems for the global FARAD program.

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Wu X, Chen Q, Chou WC, Maunsell FP, Tell LA, Baynes RE, Davis JL, Jaberi-Douraki M, Riviere JE, Lin Z. Development of a physiologically based pharmacokinetic model for flunixin in cattle and swine following dermal exposure. Toxicological Sciences. 2024 Oct 30:kfae139. https://doi.org/10.1093/toxsci/kfae139
  • Type: Other Journal Articles Status: Published Year Published: 2024 Citation: Sholehrasa H, Norouzi SS, Hitzler P, Jaberi-Douraki M. Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction. arXiv preprint arXiv:2409.14192. 2024 Sep 21. https://doi.org/10.48550/arXiv.2409.14192
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Xu X, Riviere JE, Raza S, Millagaha Gedara NI, Ampadi Ramachandran R, Tell LA, Wyckoff GJ, Jaberi-Douraki M. In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects. Expert Opinion on Drug Metabolism & Toxicology. 2024 Jan 10:1-4. https://doi.org/10.1080/17425255.2023.2299337
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2023 Citation: Nelson CE, Aramouni FM, Goering MJ, Bortoluzzi EM, Knapp LA, Herrera-Ibata DM, Li KW, Jermoumi R, Hooker JA, Sturek J, Byrd JP. Adult Ossabaw pigs prefer fermented sorghum tea over isocaloric sweetened water. Animals. 2023 Oct 18;13(20):3253. https://doi.org/10.3390/ani13203253
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Majid Jaberi-Douraki, Mohamed A. Naiel, Samantha Scott, Lucilene Bessa Rangel, Maria Eugenia Gaya Macaneiro, Mikayla Goering, Catherine Nelson, Lindsey E. Hulbert. Advancing Behavioral Research: Continuous Monitoring and Analysis of Pair-Housed Biomedical Pigs Utilizing Instance Segmentation Using Computer Vision Models. Poster Session, Swine in Biomedical Research Conference, Madison, WI, June 14  18, 2024.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Mohamed A. Naiel, Majid Jaberi-Douraki, Samantha Scott, Maria Eugenia Gaya Macaneiro, Lucilene Bessa Rangel, Rishita Praveen Shetty, Jimmy Escolero, Hossein Sholehrasa, Lindsey E. Hulbert. Exploring the Cognitive Landscape: YOLOv8-Based Object Detection for Pig Tracking in a Newly Designed 8-Radial Maze. Poster Session, Swine in Biomedical Research Conference, Madison, WI, June 14  18, 2024.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Nuwan Indika, Lisa A Tell, Jim Riviere, Raghavendra G. Amachawadi, Melissa Mercer, Remya Ampadi Ramachandran, Xuan Xu, Mobina Golmohammadi, Douglas Shane, Majid Jaberi-Douraki. Analyzing Global Fluoroquinolone Usage in Poultry Through Natural Language Processing. Poster Session, Nexus Informatics Conference, Kansas City, April 25-26, 2024.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Remya Ampadi Ramachandran, Nader Zad, Lisa A. Tell, Xuan Xu, Jim E. Riviere, Ronald Baynes, Zhoumeng Lin, Fiona Maunsell, Jennifer Davis, Majid Jaberi-Douraki. Advancing Food Safety: Predicting Maximum-Residue-Limits for Veterinary Medicines using AI/ML. Poster Session, Nexus Informatics Conference, Kansas City, April 25-26, 2024.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Remya Ampadi Ramachandran, Lisa A. Tell, Hossein Sholehrasa, Nuwan Indika Millagaha Gedara, Xuan Xu, Jim E. Riviere, Majid Jaberi-Douraki. Automated Customizable Live WebCrawler: A Smart Solution for Data Curation. Poster Session, Nexus Informatics Conference, Kansas City, April 25-26, 2024.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Catherine E. Nelson, Eduarda Mazzardo Bortoluzzi1, Mikayla J. Goering, Hui Wu, Mouhamad Alloosh, Michael Sturek, Majid Jaberi Douraki, and Lindsey E. Hulbert. Social Status Influences Adult Ossabaw Learning and Working Memory During the Development of a Novel Feeding Cognition Test. Poster Session, Swine in Biomedical Research Conference, Madison, WI, June 14  18, 2024.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Majid Jaberi-Douraki. Estimating Maximum Residue Limits for Veterinary Medicines Using Machine Learning Algorithms; The 63rd Annual Meeting of Society of Toxicology, Salt Lake City, UT. The Toxicologist, Supplement to Toxicological Sciences, 198, (S1), p. 14, abstract #: 1053. (March 10-14, 2024)
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Golmohammadi M, Coetzee J, and Bortoluzzi EM, Jaberi-Douraki M. Developing a Weighted Model for Composite Pain-Related Biomarkers in Farm Animals Undergoing Management Practices: A Large-Scale Data Analytics Approach. 6th Annual Meeting of the European Veterinary Congress of Behavioural Medicine and Animal Welfare. September 2024.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Hossein Sholehrasa, Remya Ampadi Ramachandran, Lisa A. Tell, Sidharth Rai, Nuwan Indika Millagaha Gedara, Xuan Xu, Jim E. Riviere, Hande kucuk McGinty and Majid Jaberi-Douraki. Oct 18, 2023. An Automated Customizable Live Web Crawler for Curation of Comparative Pharmacokinetic Data: An Intelligent Compilation of Research-Based Comprehensive Article Repository. Research Connections, K-State. (Poster Presentation) https://www.k-state.edu/research/faculty/otherresources/connections/
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Remya Ampadi Ramachandran, Nader Zad, Lisa A. Tell, Xuan Xu, Jim E. Riviere, Ronald Baynes, Zhoumeng Lin, Fiona Maunsell, Jennifer Davis, Majid Jaberi-Douraki. Oct 18, 2023. Harnessing Machine Learning for Veterinary Medicine Maximum Residue Limit (MRL) Estimation. Research Connections, K-State. (Poster Presentation) https://www.kstate.edu/research/faculty/other-resources/connections/
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Remya Ampadi Ramachandran, Nader Zad, Lisa A. Tell, Xuan Xu, Jim E. Riviere, Ronald Baynes, Zhoumeng Lin, Fiona Maunsell, Jennifer Davis, Majid Jaberi-Douraki. Oct. 16-18, 2023. Harnessing Machine Learning for Veterinary Medicine Maximum Residue Limit (MRL) Estimation. AI Symposium, K-State. (Poster Presentation) https://lib.kstate.edu/technology/ai-and-libraries/ai-symposium/


Progress 09/01/22 to 08/31/23

Outputs
Target Audience:Efforts include weekly meetings between the KUSO team and quarterly teaming with the other FARAD units to present and deliver new findings and science-based knowledge Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Here is the estimated FTE breakdown for the team: Two graduate students: 0.5 FTE each (Total: 1.0 FTE) Two postdocs: 0.75 FTE each (Total: 1.5 FTE) ?Overall, the team consists of 2.5 Full-Time Equivalents (FTE). How have the results been disseminated to communities of interest?The pivotal conduits for disseminating information within FARAD are the call center and internet access. These channels stand as the primary means through which timely and pertinent information is relayed to a diverse range of stakeholders, encompassing veterinarians, producers, and researchers. Their significance lies in their role in delivering vital updates. Upon the completion of a specific project, the research findings undergo a rigorous review process and subsequently find their way into peer-reviewed journals. For a comprehensive compilation of these published works, please consult the Products section. Beyond journal publications, our active involvement extends to the presentation of research findings at both local and national conferences. Through the strategic utilization of these dissemination approaches, FARAD ensures that the results of our research resonate with a wider audience, contributing substantially to progress in the realms of food safety and residue management. Our unwavering dedication centers on the advocacy of evidence-based practices and the facilitation of a culture of shared knowledge within the scientific community. What do you plan to do during the next reporting period to accomplish the goals?During the upcoming reporting period, a primary goal we're focusing on is the substantial expansion of the FARAD databases. This expansion will encompass various pharmacokinetic (PK) parameters, notably clearance and withdrawal interval, broadening the scope beyond our current focus. Our specific aim is to not only integrate clearance data from food animals but also extend our reach to encompass data from other animal species. This strategic expansion holds the potential to enrich our artificial intelligence system within the global FARAD program. By assimilating clearance data spanning diverse animal species, our research capabilities will be greatly reinforced, amplifying the overall efficacy of FARAD's data-driven methodologies. This progressive step forward will facilitate a more comprehensive and interconnected comprehension of residue management and food safety practices across a wide array of animal populations.

Impacts
What was accomplished under these goals? The goal of the national FARAD program is to protect the American public by promoting the production of safe, animal-derived human food products (milk, eggs, meat, honey, etc.) that are devoid of violative or potentially unsafe chemical residues, including drugs, pesticides, environmental contaminants, natural toxins, and other harmful substances. Kansas State University-Olathe (KSUO) will be responsible for developing interfaces by which FARAD responders can access the KSUO comparative medicine databases and associated computational tools, as well as access relevant global data. The KSUO team will pursue the goal of developing novel AI technologies practical to food-animal production for collecting, organizing, merging, and cleaning data and help easily submit queries to obtain information regarding the datasets of Global maximum residue limits (MRLs) and withdrawal periods (WDPs). They will also conduct research in designing a full-text retrieval system that will enable the creation of an information retrieval database that can be easily queried, performing metadata analysis on bibliographic records in veterinary medicine as well as those validated by the current FARAD database. This information retrieval system is significant and will be used to develop hybrid models of content-based and bibliometric features for machine learning applications to extract and clean data from different resources as an integral part of the next-generation FARAD program.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Zad N, Tell LA, Ramachandran RA, Xu X, Riviere JE, Baynes R, Lin Z, Maunsell F, Davis J, Jaberi-Douraki M. Development of machine learning algorithms to estimate maximum residue limits for veterinary medicines. Food and Chemical Toxicology. 2023 Jul 26:113920.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Zhao Y, Haley OC, Xu X, Jaberi-Douraki M, Rivard C, Pliakoni ED, Nwadike L, Bhullar M. The Potential for Cover Crops to Reduce the Load of Escherichia coli in Contaminated Agricultural Soil. Journal of Food Protection. 2023 Jul 1;86(7):100103.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Ampadi Ramachandran R, Tell LA, Rai S, Millagaha Gedara NI, Xu X, Riviere JE, Jaberi-Douraki M. An Automated Customizable Live Web Crawler for Curation of Comparative Pharmacokinetic Data: An Intelligent Compilation of Research-Based Comprehensive Article Repository. Pharmaceutics. 2023 Apr 30;15(5):1384.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Bortoluzzi EM, Goering MJ, Ochoa SJ, Holliday AJ, Mumm JM, Nelson CE, Wu H, Mote BE, Psota ET, Schmidt TB, Jaberi-Douraki M. Evaluation of Precision Livestock Technology and Human Scoring of Nursery Pigs in a Controlled Immune Challenge Experiment. Animals. 2023 Jan 10;13(2):246.


Progress 09/01/21 to 08/31/22

Outputs
Target Audience:FARAD's immediate clients are practicing veterinarians, regulators, extension officers, producers, and researchers, but it ultimately protects the food consuming public and contributes to human Public Health by equipping these professionals with the best science available. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This is a list of estimated FTE. Two graduate students: 0.5 FTE each Two postdocs: 0.75FTE each How have the results been disseminated to communities of interest?See data above on call center and internet access which is the primary route for information dissemination for FARAD. Upon completion of a specific project, we also publish the research findings in peer-reviewed journals. Please refer to the publications listed in the Products page. Also, we present our research findings in local and national conferences, such as the Annual Meeting of Society of Toxicology in 2020, 2021, and 2022 (listed in the Products page). What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, we also plan toexpand our FARAD databases to extract clearance data from food animals as well as other animals to be able to artificial intelligence system for global FARAD program.

Impacts
What was accomplished under these goals? We have expandedFARAD databases to collect Global maximum residue limits (MRLs) and withdrawal periods (WDPs). We have also developed global connection using navigation systems from HTML, XML, or pdf files and started developing artificial intelligence system for global FARAD program.

Publications

  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Mercer MA, Davis JL, Riviere JE, Baynes RE, Tell LA, Jaberi-Douraki M, Maunsell FP, Lin Z. (2022). Mechanisms of Toxicity and Residue Considerations of Rodenticide Exposure in Food Animals: a FARAD Perspective. Journal of the American Veterinary Medical Association, 260(5):514-523. [PMID: 35092661] https://doi.org/10.2460/javma.21.08.0364
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: Sidharth, Lisa Tell, Majid Jaberi-Douraki. Automatic Data Curation and Extraction from Scientific Publications. 2022 Nexus Informatics Conference.(Kansas City, MO, April 7-8, 2022).
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Richards ED, Tell LA, Davis JL, Baynes RE, Lin Z, Maunsell FP, Riviere JE, Jaberi-Douraki M, Martin KL, Davidson G. (2021). Honey bee medicine for veterinarians and guidance for avoiding violative chemical residues in honey. Journal of the American Veterinary Medical Association, 259(8):860-873. [PMID: 34609191] https://doi.org/10.2460/javma.259.8.860
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Maunsell, F., Baynes, R., Davis, J., Foster, D., Jaberi-Douraki, M., Riviere, J., & Tell, L. (2021, October). FARAD: How we respond to withdrawal inquiries. In American Association of Bovine Practitioners Conference Proceedings (pp. 9-11).