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

110
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...
110
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

299
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
299
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

122
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...
122
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

148
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...
148
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

607
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:
607
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

115
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
115

You might also read

Related Articles

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

Sort by
Same author

Development of a signal quality evaluation of dynamic versus static <sup>18</sup>FDG-PET in focal epilepsy via Bayesian regional estimated signal quality analysis.

AJNR. American journal of neuroradiology·2026
Same author

Does lumbar vertebra bone microstructure relate to combined loading fracture tolerance and inform fracture initiation site?

Bone·2026
Same author

For infants with surgical necrotizing enterocolitis, does primary anastomosis or stoma formation provide shorter parenteral nutrition?

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

Solar ultraviolet radiation exposure, and incidence of childhood (0-19 years) malignant and non-malignant brain tumour in a US population-based dataset, 2000-2021.

European journal of epidemiology·2025
Same author

Contemplative Training to Bolster University Employees' Mental Health, Well-Being, and Workplace Wellness: A Prospective Observational Study.

Journal of occupational and environmental medicine·2025
Same author

Injury patterns and seat belt effectiveness in pregnant motor vehicle occupants: evidence from US crash data, 1998-2021.

Injury epidemiology·2025

Related Experiment Video

Updated: Oct 16, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.4K

Partitioning macroscale and microscale ecological processes using covariate-driven non-stationary spatial models.

Charlotte F Narr1,2, Pavel Chernyavskiy3, Sarah M Collins2

  • 1Southern Illinois University in Carbondale, Carbondale, Illinois, 62901, USA.

Ecological Applications : a Publication of the Ecological Society of America
|October 22, 2021
PubMed
Summary

Ecologists can now better understand lake eutrophication using improved non-stationary models. These models visualize spatial patterns and explain how land use affects nutrient and algae levels across regions.

Keywords:
LAGOS databasechlorophyll aeutrophicationnon-stationary modelphosphorus

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.5K

Related Experiment Videos

Last Updated: Oct 16, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.4K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.5K

Area of Science:

  • Ecology
  • Environmental Science
  • Spatial Statistics

Background:

  • Ecological inference often requires integrating data across spatial scales, leading to complex spatial dependence structures.
  • Fully non-stationary models accurately represent these structures but are challenging for ecologists to estimate and interpret.
  • Lake eutrophication is a pervasive environmental issue driven by complex spatial processes.

Purpose of the Study:

  • To improve the interpretability of a recently developed non-stationary model for ecological applications.
  • To apply the enhanced model to understand spatial processes driving lake eutrophication in different US regions.
  • To visualize and explain the spatial dependence structure of environmental variables using ecologically relevant covariates.

Main Methods:

  • Reformulated a non-stationary model by incorporating environmental predictors into the covariance function.
  • Developed visual tools (ellipses) to represent spatial correlation range and directionality.
  • Applied the model to analyze total phosphorus and chlorophyll a spatial structures in the Midwest and Northeast US.

Main Results:

  • In the Midwest, forest cover influenced total phosphorus spatial homogeneity (macroscale processes), while forest cover and baseflow affected chlorophyll a spatial homogeneity (microscale processes).
  • In the Northeast, urban land use and baseflow decreased phosphorus concentration homogeneity (microscale processes), but covariates did not strongly explain chlorophyll a spatial structure.
  • The study demonstrated that spatial dependence structures shift across space and can be explained by covariates from different scales.

Conclusions:

  • The enhanced non-stationary model facilitates the use and interpretation of complex spatial models in ecology.
  • The findings provide novel insights into the scale-dependent spatial processes driving lake eutrophication.
  • Understanding these spatial dynamics is crucial for managing complex environmental issues like eutrophication.