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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

70
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
70
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

440
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
440
The Availability Heuristic01:08

The Availability Heuristic

6.1K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.1K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

151
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
151
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

106
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
106
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

132
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
132

You might also read

Related Articles

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

Sort by
Same author

A curated geospatial dataset of chemicals used in hydraulic fracturing and their functions.

Journal of environmental quality·2026
Same author

Recommendations for temporal aggregation of water quality data from multi-platform satellite constellations.

International journal of remote sensing·2026
Same author

Characterizing Accuracy of Model Predictions for Chemical Concentration in High Throughput Screening Assays.

Environmental science & technology·2026
Same author

Performance evaluation and methods comparison of transcriptomic-based approaches for the characterization of wastewater treatment effluent.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

Correction to: Examining the effects of analytical replication on data quality in a non‑targeted analysis experiment.

Analytical and bioanalytical chemistry·2025
Same author

Curating and Visualizing the Analytical Methods and the Open Spectral Database's Chemical Functional Use Taxonomy.

Journal of chemical information and modeling·2025

Related Experiment Video

Updated: Aug 4, 2025

Whole-Body Nanoparticle Aerosol Inhalation Exposures
10:11

Whole-Body Nanoparticle Aerosol Inhalation Exposures

Published on: May 7, 2013

15.9K

A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data.

Jeffrey M Minucci1, S Thomas Purucker2, Kristin K Isaacs2

  • 1Center for Public Health and Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Drive, Durham, North Carolina 27709, United States.

Environmental Science & Technology
|March 30, 2023
PubMed
Summary

This study introduces a data-driven model to estimate chemical exposure in workplaces. The approach accurately predicts air concentrations, aiding in chemical risk assessment and prioritization.

Keywords:
Bayesianair monitoringhierarchical modelhigh-throughputoccupational exposurescreening

More Related Videos

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA
12:02

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

12.5K
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K

Related Experiment Videos

Last Updated: Aug 4, 2025

Whole-Body Nanoparticle Aerosol Inhalation Exposures
10:11

Whole-Body Nanoparticle Aerosol Inhalation Exposures

Published on: May 7, 2013

15.9K
Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA
12:02

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

12.5K
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K

Area of Science:

  • Environmental Health
  • Occupational Safety
  • Computational Toxicology

Background:

  • Thousands of chemicals require exposure assessment for health hazard evaluation.
  • Existing methods struggle with the rapid assessment of numerous substances.
  • High-throughput screening and data-driven approaches are needed for chemical safety.

Purpose of the Study:

  • To develop a predictive model for estimating occupational chemical exposure.
  • To utilize a large database of workplace air samples for exposure modeling.
  • To support high-throughput chemical prioritization efforts.

Main Methods:

  • A Bayesian hierarchical model was developed using industry type and chemical properties.
  • The model predicts the distribution of chemical concentrations in workplace air.
  • A dataset of over 1.5 million U.S. workplace air samples was utilized.

Main Results:

  • The model achieved 75.9% classification accuracy in predicting chemical detection.
  • A root-mean-square error (RMSE) of 1.00 log10 mg m-3 was obtained for concentration prediction.
  • Predictions were successfully generated for 5587 new substance-workplace pairs.

Conclusions:

  • The developed model offers a robust method for estimating occupational exposure.
  • This approach enhances the consideration of exposure in risk-based chemical prioritization.
  • The framework supports the assessment of new chemical substances under regulatory frameworks like TSCA.