Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

580
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
580
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
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

708
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...
708
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2026
Same author

ACCELERATING QUANTITATIVE MRI USING SUBSPACE MULTISCALE ENERGY MODEL (SS-MUSE).

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

FAST MULTI-CONTRAST MRI USING JOINT MULTISCALE ENERGY MODEL.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

Accelerating 3D radial MPnRAGE using a self-supervised deep factor model.

Magnetic resonance in medicine·2025
Same author

Multi-Scale Energy (MuSE) framework for inverse problems in imaging.

IEEE transactions on computational imaging·2025
Same author

Fast and ultra-high shot diffusion MRI image reconstruction with self-adaptive Hankel subspace.

Medical image analysis·2025

Video Experimental Relacionado

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635

RED POSTERIORA DE FONDO A FONDO (DEEPEN) para los problemas invertidos

Jyothi Rikhab Chand1, Mathews Jacob1

  • 1Department of Electrical and Computer Engineering, University of Iowa, IA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 29, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Desarrollamos un método de aprendizaje profundo eficiente para la reconstrucción de imágenes por resonancia magnética (RM). Este enfoque permite aprender la distribución posterior, mejorar la recuperación de imágenes y proporcionar mapas de incertidumbre.

Palabras clave:
Modelo energéticoEstimación del PMAReconstrucción de resonancia magnética paralelaEstimación de la incertidumbre

Más Videos Relacionados

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Videos de Experimentos Relacionados

Last Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Área de la Ciencia:

  • Imágenes médicas
  • Neurociencia computacional
  • Aprendizaje automático

Sus antecedentes:

  • Los marcos de optimización desenrollados de extremo a extremo (E2E) son prometedores para la recuperación de imágenes de resonancia magnética (MR).
  • Estos métodos deterministas enfrentan desafíos con un alto uso de memoria durante el entrenamiento y carecen de capacidades de muestreo de distribución posterior.

Objetivo del estudio:

  • Introducir un enfoque de memoria eficiente para el aprendizaje E2E de la distribución posterior en la reconstrucción de imágenes de RM.
  • Para permitir la cuantificación de la incertidumbre junto con la recuperación de imágenes.

Principales métodos:

  • Un nuevo marco que combina un término de probabilidad de consistencia de datos y un modelo de energía previa parametrizado por CNN.
  • Aprendizaje E2E de las ponderaciones de CNN a través de la optimización de la máxima probabilidad.
  • Optimización máxima a posteriori (MAP) para la recuperación de imágenes a partir de datos de resonancia magnética submuestreados.

Principales resultados:

  • El método propuesto obtiene un rendimiento comparable al de los algoritmos desenrollados E2E intensivos en memoria.
  • Supera a las contrapartes existentes de memoria eficiente en la reconstrucción de imágenes de RM.
  • El marco genera con éxito mapas de incertidumbre derivados del muestreo de distribución posterior.

Conclusiones:

  • Este marco de aprendizaje E2E eficiente en la memoria avanza en la reconstrucción de imágenes de RM.
  • Ofrece una solución viable para imágenes de resonancia magnética de alta dimensión (3D +).
  • La capacidad de muestrear la distribución posterior proporciona información valiosa sobre la incertidumbre.