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

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

4.8K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
4.8K
X-ray Crystallography02:18

X-ray Crystallography

26.2K
The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
26.2K
Interference and Diffraction02:18

Interference and Diffraction

52.5K
Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
52.5K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

570
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...
570
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

279
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...
279
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

335
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
335

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Generation of the Camptothecin Scaffold by a Flavin-Catalyzed Photooxidative Skeletal Reorganization.

Angewandte Chemie (International ed. in English)·2026
Same author

The Missing Block: Regional Anaesthesia as the Unseen Driver of Post-Thoracoscopic Pain Trajectories.

European journal of pain (London, England)·2026
Same author

The Arabidopsis transcription factor TCP4 controls seed size by repressing the MINI3-SHB1-IKU pathway.

The Plant cell·2026
Same author

Leveraging Large Language Models to Identify Engagement-Driving Features in Vaping-Related TikTok Videos: Cross-Sectional Study.

Journal of medical Internet research·2025
Same author

Predicting the risk of asthma development in youth using machine learning models.

PloS one·2025
Same author

VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2025

Video Experimental Relacionado

Updated: Feb 7, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Enfoques de aprendizaje automático para la clasificación cristalográfica a partir de datos sintéticos de difracción

Ayoub Shahnazari1, Zeliang Zhang2, Sachith E Dissanayake3

  • 1Department of Mechanical Engineering University of Rochester Rochester New York14627 USA.

Journal of applied crystallography
|February 6, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo método que utiliza patrones sintéticos de difracción de rayos X 2D (XRD) y aprendizaje profundo (DL) para la identificación rápida y automatizada de estructuras cristalográficas. Este enfoque acelera la investigación en ciencia de materiales al superar las limitaciones del análisis tradicional.

Palabras clave:
Pipeline de Difracción AutomáticaCIFsCNNsredes neuronales convolucionalesclasificación de sistemas cristalinosarchivos de información cristalográficaclasificación de grupos espacialespatrones sintéticos de difracción de rayos X 2D

Más Videos Relacionados

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Crystallization of Proteins on Chip by Microdialysis for In Situ X-ray Diffraction Studies
12:38

Crystallization of Proteins on Chip by Microdialysis for In Situ X-ray Diffraction Studies

Published on: April 11, 2021

7.0K

Videos de Experimentos Relacionados

Last Updated: Feb 7, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Crystallization of Proteins on Chip by Microdialysis for In Situ X-ray Diffraction Studies
12:38

Crystallization of Proteins on Chip by Microdialysis for In Situ X-ray Diffraction Studies

Published on: April 11, 2021

7.0K

Área de la Ciencia:

  • Ciencia de Materiales
  • Cristalografía
  • Ciencia Computacional

Sus antecedentes:

  • La identificación de estructuras cristalográficas es vital para las propiedades de los materiales.
  • El análisis actual de patrones de difracción de rayos X 2D (XRD) requiere mucho tiempo y mano de obra.
  • La escasez de datos experimentales dificulta un análisis exhaustivo.

Objetivo del estudio:

  • Desarrollar un método automatizado y de alto rendimiento para clasificar sistemas cristalinos y grupos espaciales.
  • Aprovechar los patrones sintéticos de difracción de rayos X 2D y el aprendizaje profundo (DL) para mejorar el análisis cristalográfico.
  • Introducir el Pipeline de Difracción Automática para generar datos sintéticos realistas de difracción de rayos X.

Principales métodos:

  • Generación de patrones de difracción de rayos X 2D sintéticos utilizando el Pipeline de Difracción Automática.
  • Inclusión de diversas condiciones (ejes de zona, variaciones atómicas, carga mecánica) para mejorar el realismo de los datos sintéticos.
  • Entrenamiento y validación de redes neuronales convolucionales en conjuntos de datos sintéticos para la clasificación de estructuras.

Principales resultados:

  • Se demostró una clasificación rápida y precisa de las estructuras cristalográficas.
  • Se clasificaron con éxito siete sistemas cristalinos y 230 grupos espaciales utilizando datos sintéticos y DL.
  • Se validó la efectividad del Pipeline de Difracción Automática en la creación de conjuntos de entrenamiento grandes y representativos.

Conclusiones:

  • La integración de patrones sintéticos de difracción de rayos X 2D con DL permite una clasificación cristalográfica automatizada y eficiente.
  • Este enfoque basado en datos supera la escasez de datos experimentales y los cuellos de botella de análisis.
  • Promueve una mayor adopción de métodos computacionales en la ciencia de materiales para la identificación de estructuras.