Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Healthcare Associated Infections II: Preventive Measures01:22

Healthcare Associated Infections II: Preventive Measures

Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
Microbes in Food Production01:29

Microbes in Food Production

Microbial fermentation is central to food biotechnology, enhancing flavor, texture, preservation, and stability. Fermentative microorganisms metabolize carbohydrates into organic acids, alcohols, and other metabolites that inhibit spoilage organisms and improve digestibility while contributing distinctive sensory qualities.In baking, amylases naturally present in flour hydrolyze starch into monosaccharides such as glucose, which Saccharomyces cerevisiae ferments anaerobically. Through...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Inhibition of the QPCT-PDIA4 axis rescues ΔF508 and N1303K CFTR in cystic fibrosis.

Nature communications·2026
Same author

Testosterone and cancers: biological functions, molecular mechanisms and therapy.

Molecular cancer·2026
Same author

PKFAR: psychiatry knowledge-fused augmented reasoning with large language models.

Health information science and systems·2026
Same author

The correlation between blood urea nitrogen to creatinine ratio and short-term and long-term all-cause mortality in sepsis-associated acute kidney injury patients aged 50 and above: a large retrospective cohort study.

Frontiers in endocrinology·2026
Same author

Magnetic-Actuation-Enhanced Indirect Photoacoustic Sensing for Real-Time Homogeneous Immunoassay.

ACS sensors·2026
Same author

Screening of Salivary Biomarkers of Bisphosphonate-Related Osteonecrosis of the Jaw in a Diabetic Rat Model.

Current issues in molecular biology·2026
Same journal

Modelling the process and formulation conditions which prevent the outgrowth of non-proteolytic Clostridium botulinum in chilled foods.

Journal of food protection·2026
Same journal

Prevalence of Fecal Indicators and Viruses on Touchscreens and Other High-Touch Surfaces in Retail Food Establishments in the United States.

Journal of food protection·2026
Same journal

Addressing Regulatory Gaps: Comprehensive Residue Survey & Risk Assessment for the Specialty Crop Ziziphus mauritiana Lam. in Hainan and Guangdong, China.

Journal of food protection·2026
Same journal

Epidemiology and Control of Foodborne Diseases in Resource-Limited Settings: Insights from Afghanistan.

Journal of food protection·2026
Same journal

Microbial Food Safety Risk in Finished Soil Amendments: A Statewide Cross-Sectional Study of Organic Waste Processing Systems in California.

Journal of food protection·2026
Same journal

Ten-Year Surveillance of Listeria monocytogenes in Ready-to-Eat Meat Products in Montenegro: Prevalence, NonCompliance, and Risk Patterns.

Journal of food protection·2026
See all related articles
  1. Home
  3. Class-incremental Learning For Foodborne Pathogen Prediction Based On Clinical Surveillance Data.
  1. Home
  3. Class-incremental Learning For Foodborne Pathogen Prediction Based On Clinical Surveillance Data.

Related Experiment Video

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products
08:19

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Published on: January 31, 2025

Class-Incremental Learning for Foodborne Pathogen Prediction Based on Clinical Surveillance Data.

Ke Qin1, Linhai Wu2, Minguo Gao3

  • 1School of Business, Jiangnan University, No.1800, Lihu Avenue, Wuxi 214122, PR China.

Journal of Food Protection
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces CIL-DAFFNet, a novel AI model for identifying foodborne pathogens. It effectively handles imbalanced data and emerging threats, improving accuracy in pathogen detection for food safety.

Keywords:
Class-incremental learningFew-shot learningFoodborne diseasesFoodborne pathogensImbalanced data

More Related Videos

Detection of Foodborne Bacterial Pathogens from Individual Filth Flies
12:54

Detection of Foodborne Bacterial Pathogens from Individual Filth Flies

Published on: February 13, 2015

Related Experiment Videos

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products
08:19

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Published on: January 31, 2025

Detection of Foodborne Bacterial Pathogens from Individual Filth Flies
12:54

Detection of Foodborne Bacterial Pathogens from Individual Filth Flies

Published on: February 13, 2015

Area of Science:

  • Microbiology
  • Computer Science
  • Public Health

Background:

  • Foodborne diseases (FBDs) present a major global health concern, requiring swift and precise identification of causative agents.
  • Current pathogen detection methods struggle with challenges like limited data for rare pathogens, imbalanced datasets, and the need to adapt to new pathogen discoveries.

Purpose of the Study:

  • To develop an advanced Class Incremental Learning (CIL) model, the CIL-DAFFNet, designed to overcome limitations in real-world foodborne pathogen identification.
  • To enhance the accuracy and adaptability of pathogen detection systems for improved food safety and clinical diagnostics.

Main Methods:

  • Proposed the Class Incremental Learning with Dual Attention and Adaptive Feature Fusion Network (CIL-DAFFNet).
  • Integrated a dual-attention mechanism for improved feature extraction from imbalanced clinical data.
  • Utilized a dynamically weighted knowledge distillation strategy with Maximum Mean Discrepancy (MMD) to prevent catastrophic forgetting in incremental learning.

    • Main Results:

    • CIL-DAFFNet demonstrated superior performance against five incremental learning baselines on a real-world clinical dataset.
    • Achieved high accuracy (0.8245), Macro-F1 score (0.8004), and G-mean (0.7972) in the final incremental phase.
    • Ablation studies and SHAP analysis validated the model's effectiveness and provided interpretable feature importance.

    Conclusions:

    • CIL-DAFFNet offers a robust solution for the rapid and accurate identification of foodborne pathogens, addressing key real-world challenges.
    • The model supports intelligent food safety monitoring and aids clinical decision-making by providing reliable pathogen detection.
    • This research advances the field of incremental learning for public health applications, particularly in combating foodborne illnesses.