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

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

2.8K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
2.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

372
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...
372
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

2.0K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
2.0K
Integration by Parts: Problem Solving01:29

Integration by Parts: Problem Solving

85
Smart speakers process voice commands by modeling audio inputs as piecewise functions and analyzing them through integration against trigonometric functions, such as cosine. This mathematical approach is fundamental in signal processing, where complex sound waves are decomposed into simpler frequency components.Consider a definite integral involving a piecewise function multiplied by a cosine function. Because the function is defined differently over separate intervals, the integral is split...
85
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

7.0K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
7.0K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.3K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida.

Nature communications·2025
Same author

Enabling malic acid production from corn-stover hydrolysate in Lipomyces starkeyi via metabolic engineering and bioprocess optimization.

Microbial cell factories·2025
Same author

Author Correction: Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.

Communications biology·2025
Same author

Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.

Communications biology·2025
Same author

Quantum Weak Values and the "Which Way?" Question.

Entropy (Basel, Switzerland)·2025
Same author

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

PLoS computational biology·2023

Related Experiment Video

Updated: Feb 27, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.4K

Adaptive Splitting Integrators for Enhancing Sampling Efficiency of Modified Hamiltonian Monte Carlo Methods in

Elena Akhmatskaya1,2, Mario Fernández-Pendás1, Tijana Radivojević1

  • 1BCAM - Basque Center for Applied Mathematics , Alameda de Mazarredo 14, E-48009 Bilbao, Spain.

Langmuir : the ACS Journal of Surfaces and Colloids
|July 11, 2017
PubMed
Summary

Modified Adaptive Integration Approach (MAIA) and its extension (e-MAIA) optimize molecular simulation parameters. These methods enhance sampling efficiency in modified Hamiltonian Monte Carlo (MHMC) simulations without computational overhead.

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.8K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.7K

Related Experiment Videos

Last Updated: Feb 27, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.4K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.8K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.7K

Area of Science:

  • Computational chemistry and physics
  • Molecular dynamics and simulation
  • Statistical mechanics

Background:

  • Modified Hamiltonian Monte Carlo (MHMC) methods offer superior sampling efficiency compared to standard molecular dynamics (MD) and Hybrid Monte Carlo (HMC).
  • Optimizing simulation parameters and employing advanced splitting algorithms can further enhance MHMC performance.
  • Identifying appropriate parameter values for these advanced algorithms is challenging.

Purpose of the Study:

  • To introduce the Modified Adaptive Integration Approach (MAIA) for automatic selection of optimal integrators in MHMC simulations.
  • To present Extended MAIA (e-MAIA) for adaptive parameter selection to maintain desired momentum acceptance rates.
  • To implement and evaluate MAIA and e-MAIA in the context of molecular simulations.

Main Methods:

  • Development of MAIA and e-MAIA algorithms for adaptive integration and parameter selection.
  • Implementation of MAIA and e-MAIA within the MultiHMC-GROMACS software package.
  • Testing and comparison against standard and advanced integrators using established molecular models.

Main Results:

  • MAIA and e-MAIA algorithms were successfully implemented with no computational overhead during simulations.
  • The proposed methods demonstrated superior performance over various integrators, including recently developed ones.
  • Enhanced sampling efficiency was observed for Generalized Split Hamiltonian Monte Carlo (GSHMC) when combined with e-MAIA.

Conclusions:

  • MAIA and e-MAIA provide effective solutions for optimizing simulation parameters in MHMC methods.
  • These adaptive approaches significantly improve sampling efficiency, particularly when combined with methods like GSHMC.
  • The integration of MAIA/e-MAIA into GROMACS offers a practical tool for advanced molecular simulations.