Source: KANSAS STATE UNIV submitted to
FOOD ANIMAL RESIDUE AVOIDANCE DATABANK (FARAD)
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
OTHER GRANTS
Reporting Frequency
Annual
Accession No.
1029006
Grant No.
2022-41480-38135
Project No.
KS53222022
Proposal No.
2022-05990
Multistate No.
(N/A)
Program Code
FARAD
Project Start Date
Sep 1, 2022
Project End Date
Aug 31, 2025
Grant Year
2022
Project Director
Jaberi-Douraki, M.
Recipient Organization
KANSAS STATE UNIV
(N/A)
MANHATTAN,KS 66506
Performing Department
(N/A)
Non Technical Summary
The FARAD program is a national food safety program funded for 40 years since 1982 by USDA. The program is 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 2022-2023, 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 access 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
100%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
7115010118020%
7113910208020%
7113910209020%
5023910208020%
5023910209020%
Knowledge Area
711 - Ensure Food Products Free of Harmful Chemicals, Including Residues from Agricultural and Other Sources; 502 - New and Improved Food Products;

Subject Of Investigation
3910 - Cross-commodity research--multiple animal species; 5010 - Food;

Field Of Science
2090 - Statistics, econometrics, and biometrics; 1180 - Pharmacology; 2080 - Mathematics and computer sciences;
Goals / Objectives
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.
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 techniques 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 09/01/23 to 08/31/24

Outputs
Target Audience:Our target audiences include veterinarians, producers, researchers, and students at all levels, from undergraduates to graduate researchers. These students will receive training and mentorship throughout the project. Efforts to foster collaboration and knowledge-sharing include weekly meetings within the KUSO team and quarterly sessions with other FARAD units to present and share new findings and scientific insights. 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?FARAD utilizes call centers and internet access as the primary channels for disseminating information to stakeholders, including veterinarians, producers, and researchers. These platforms ensure the timely distribution of relevant data and updates. Upon completing specific projects, research findings are published in peer-reviewed journals, with a comprehensive list available in the Products section. We also actively present our findings at local and national conferences. By leveraging these dissemination methods, FARAD ensures that our research outcomes reach a wider audience and contribute to advancements in food safety and residue management. We are committed to promoting evidence-based practices and fostering knowledge-sharing within the scientific community. What do you plan to do during the next reporting period to accomplish the goals?In the upcoming reporting period, a key objective is to significantly expand the FARAD databases to include additional pharmacokinetic (PK) parameters, specifically focusing on clearance and withdrawal intervals. Our goal is to incorporate clearance data not only from food animals but also from other animal species. This expansion will provide access to a broader range of data, thereby enhancing the capabilities of our artificial intelligence system within the global FARAD program. By including clearance data from various animal species, we aim to bolster our research capabilities and improve the overall effectiveness of FARAD's data-driven approaches. This development will enable a more comprehensive and holistic understanding of residue management and food safety practices across diverse animal populations.

Impacts
What was accomplished under these goals? The FARAD databases have been expanded to include Global Maximum Residue Limits (MRLs) and Withdrawal Periods (WDPs). This expansion provides a more comprehensive and diverse range of data, enhancing our research capabilities. To facilitate global access to FARAD information, we have developed navigation systems using HTML, XML, and PDF files. These systems allow for efficient and seamless access to essential data, making it easier for stakeholders worldwide to benefit from FARAD resources. For more details on our progress, please visit: https://1data.life/pages/publication/AI_Algorithms_to_Estimate_MRLs.html.

Publications

  • Type: 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.
  • Type: Conference Papers and Presentations Status: Other 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: Other 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: Other 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. Harnessing Machine Learning for Veterinary Medicine Maximum Residue Limit Estimation. Poster Session, K-State AI Symposium, October 16th 2023.
  • Type: 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, Jaberi-Douraki M, Hulbert LE. Adult Ossabaw pigs prefer fermented sorghum tea over isocaloric sweetened water. Animals. 2023 Oct 18;13(20):3253.
  • Type: Conference Papers and Presentations Status: Other 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: 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)


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 call center and internet access serve as the primary routes for information dissemination within FARAD. These channels play a crucial role in providing timely and relevant information to various stakeholders, including veterinarians, producers, and researchers.Furthermore, once a specific project is completed, the research findings are published in peer-reviewed journals. To access a comprehensive list of these publications, kindly refer to the Products section.In addition to journal publications, we actively engage in presenting our research findings at both local and national conferences. By leveraging these dissemination methods, FARAD ensures that our research outcomes reach a broader audience and contribute to advancements in the field of food safety and residue management. We remain committed to promoting evidence-based practices and fostering knowledge-sharing within the scientific community. What do you plan to do during the next reporting period to accomplish the goals?In the upcoming reporting period, one of our key objectives is to expand the FARAD databases significantly on other PK parameters including the clearance and withdrawal interval. Specifically, we aim to include clearance data from not only food animals but also other animal species. This expansion will enable us to access a broader range of data and enhance the capabilities of our artificial intelligence system for the global FARAD program.By incorporating clearance data from various animal species, we will bolster our research capabilities and strengthen the overall effectiveness of FARAD's data-driven approaches. This development will facilitate a more comprehensive and holistic understanding of residue management and food safety practices across diverse animal populations.

Impacts
What was accomplished under these goals? The FARAD databases have been expanded to encompass Global maximum residue limits (MRLs) and withdrawal periods (WDPs). This expansion enables us to access a more comprehensive and diverse range of data, contributing to the enhancement of our research capabilities. As part of our efforts to facilitate global access to FARAD information, we have also developed navigation systems utilizing HTML, XML, or pdf files. This development allows for efficient and seamless access to essential data, making it easier for stakeholders worldwide to benefit from FARAD resources. For more details on our progress, please visit: https://1data.life/pages/publication/AI_Algorithms_to_Estimate_MRLs.html

Publications

  • Type: Journal Articles Status: Accepted 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.