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

Sulfur Assimilation01:20

Sulfur Assimilation

378
Sulfur is an essential element in biological systems, contributing to synthesizing key biomolecules, including amino acids such as cysteine and methionine, and cofactors such as coenzyme A and biotin. Microorganisms primarily assimilate sulfur as sulfate (SO₄²⁻) from the environment, which must undergo a series of biochemical transformations before it can be incorporated into cellular components. As sulfate is highly oxidized, it must undergo assimilatory sulfate reduction to...
378
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

564
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
564
Inorganic Nitrogen Assimilation01:22

Inorganic Nitrogen Assimilation

582
Nitrogen is an essential element in biological systems, forming a crucial component of proteins, nucleic acids, and other cellular constituents. Many bacteria and archaea acquire nitrogen in the form of nitrate (NO₃⁻) or ammonia (NH₃), which are then assimilated into biomolecules through specific enzymatic pathways.Assimilatory Nitrate ReductionWhen nitrate enters the cell, it undergoes a two-step reduction process known as assimilatory nitrate reduction. Initially, the enzyme...
582
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

997
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:
997
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

296
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
296
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

598
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
598

You might also read

Related Articles

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

Sort by
Same author

Molecular characterization and expression of SiFad1 in the sea urchin (Strongylocentrotus intermedius).

Gene·2019
Same author

Diamondoid Frameworks via Supramolecular Coordination: Structural Characterization, Metallogel Formation, and Adsorption Study.

Inorganic chemistry·2019
Same author

Phthalate side-chain structures and hydrolysis metabolism associated with steroidogenic effects in MLTC-1 Leydig cells.

Toxicology letters·2019
Same author

A New Perspective on the Effect of UV-B on l-Ascorbic Acid Metabolism in Cucumber Seedlings.

Journal of agricultural and food chemistry·2019
Same author

Proteomic investigations of human HERC2 mutants: Insights into the pathobiology of a neurodevelopmental disorder.

Biochemical and biophysical research communications·2019
Same author

Grape seed proanthocyanidin inhibits monocrotaline-induced pulmonary arterial hypertension via attenuating inflammation: in vivo and in vitro studies.

The Journal of nutritional biochemistry·2019

Related Experiment Video

Updated: Feb 14, 2026

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data
11:09

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Published on: February 25, 2021

3.9K

A new data assimilation method for high-dimensional models.

Guangjie Wang1,2, Xiaoqun Cao1,2, Xun Cai1,2

  • 1College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China.

Plos One
|February 9, 2018
PubMed
Summary
This summary is machine-generated.

A new dual number automatic differentiation (AD) method simplifies gradient computation in variational data assimilation (VarDA). This approach overcomes the limitations of traditional adjoint methods for complex models, enabling accurate initial condition reconstruction.

More Related Videos

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.4K

Related Experiment Videos

Last Updated: Feb 14, 2026

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data
11:09

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Published on: February 25, 2021

3.9K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.4K

Area of Science:

  • Geosciences
  • Computational Science
  • Applied Mathematics

Background:

  • Variational data assimilation (VarDA) commonly uses the adjoint method for gradient computation.
  • The adjoint method presents challenges in accuracy, implementation, and complexity for high-dimensional models.

Purpose of the Study:

  • To introduce a novel data assimilation method using dual number automatic differentiation (AD).
  • To address the limitations of the adjoint method in gradient computation for high-dimensional models.

Main Methods:

  • Dual number automatic differentiation (AD) for gradient computation.
  • Elimination of the need for tangent-linear/adjoint model coding.
  • Simultaneous computation of cost function and gradient via forward computation in dual number space.

Main Results:

  • Numerical simulations performed on nonlinear advection and parabolic equations.
  • Successful and accurate reconstruction of initial conditions for high-dimensional nonlinear dynamical systems.
  • Rapid convergence of estimated initial values to true values, even with noisy observations.

Conclusions:

  • The proposed dual number AD method offers a convenient and accurate alternative for gradient computation in VarDA.
  • This method simplifies the data assimilation process for complex, high-dimensional models.
  • The technique demonstrates robustness in reconstructing initial conditions under noisy observational data.