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

321
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...
321
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

393
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
393
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

56
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
56
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

905
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
905
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
310
Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

61
The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration,...
61

You might also read

Related Articles

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

Sort by
Same author

Muscle mitochondria, function, mass, and quality of life in prostate cancer during androgen deprivation therapy.

Nature communications·2026
Same author

Long-Range Transverse-Momentum Correlations and Radial Flow in Pb-Pb Collisions at the LHC.

Physical review letters·2026
Same author

Search for Quasiparticle Scattering in the Quark-Gluon Plasma with Jet Splittings in pp and Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

Physical review letters·2025
Same author

First Measurement of A=4 Hypernuclei and Antihypernuclei at the LHC.

Physical review letters·2025
Same author

Pregnant women with periodontal disease: can complete blood count be useful?

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas·2025
Same author

Probing Strangeness Hadronization with Event-by-Event Production of Multistrange Hadrons.

Physical review letters·2025

Related Experiment Video

Updated: Mar 26, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

J M Garcia1, F Teodoro1,2, R Cerdeira1

  • 1a Escola Superior de Tecnologia de Setúbal , Instituto Politécnico , Setúbal , Portugal.

Environmental Technology
|February 4, 2016
PubMed
Summary

This study presents a generalized linear model (GLM) to predict PM10 concentrations using air pollutant and meteorological data. Models using air temperature above 25°C showed the best performance for forecasting air quality.

Keywords:
Outdoor air qualityPM10SPSSgeneralized linear methodsmethodology

More Related Videos

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure
06:52

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

Published on: July 19, 2018

7.0K
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

3.1K

Related Experiment Videos

Last Updated: Mar 26, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure
06:52

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

Published on: July 19, 2018

7.0K
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

3.1K

Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Air Quality Monitoring

Background:

  • Particulate Matter (PM10) concentrations are critical indicators of urban air quality.
  • Accurate prediction of PM10 levels is essential for public health and environmental management.
  • Existing models may have limitations in diverse meteorological conditions.

Purpose of the Study:

  • To develop and validate a methodology for predicting urban outdoor PM10 concentrations.
  • To establish relationships between gaseous pollutants, meteorological variables, and PM10 levels.
  • To assess model performance across different temperature ranges.

Main Methods:

  • Generalized Linear Models (GLMs) were employed to predict PM10 concentrations.
  • Data from Portuguese air quality monitoring stations in Barreiro and Oporto were utilized.
  • Independent variables included gaseous pollutants (CO, NO2, NOx, VOCs, SO2) and meteorological factors (temperature, RH, wind speed).
  • A logarithmic link function with a Poisson distribution was applied, with specific focus on temperatures above and below 25°C.

Main Results:

  • The GLM successfully correlated gaseous pollutants and meteorological variables with PM10 concentrations.
  • Models incorporating air temperature above 25°C demonstrated superior predictive performance compared to models using all temperature ranges or only temperatures below 25°C.
  • The methodology showed similar performance when tested in Oporto, indicating its generalizability.

Conclusions:

  • The developed GLM methodology provides a reliable approach for predicting urban PM10 concentrations.
  • This model can be adopted in other cities lacking direct air quality monitoring data.
  • Temperature, particularly above 25°C, is a significant factor in PM10 prediction models.