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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Two-Dimensional (2D) NMR: Overview01:12

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Video Experimental Relacionado

Updated: Feb 7, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Software de aprendizaje profundo y modelo 2D revisado para segmentar huesos en escaneos de micro-CT

Andrew H Lee1,2,3,4, Ganesh Talluri5, Manan Damani4

  • 1Department of Anatomy, College of Graduate Studies, Midwestern University, Glendale, AZ, United States.

Frontiers in bioinformatics
|February 6, 2026
PubMed
Resumen

El modelo revisado de aprendizaje profundo, BP-2D-03, mejora la segmentación ósea en escaneos de micro-CT en diversas especies y condiciones. El software BONe DL proporciona resultados sólidos y reproducibles, mejorando el análisis automatizado de la porosidad ósea.

Palabras clave:
inteligencia artificialavizohuesomédula óseamamíferosegmentación semántica

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Área de la Ciencia:

  • Imagen Biomédica
  • Biología Computacional
  • Aprendizaje Automático

Sus antecedentes:

  • El aprendizaje profundo (DL) tiene dificultades con la generalización de la segmentación ósea en micro-CT en conjuntos de datos variados.
  • Los modelos de DL existentes enfrentan desafíos con la fuga de datos, el alto uso de memoria y el soporte limitado para múltiples GPU.

Objetivo del estudio:

  • Presentar BP-2D-03, un modelo de segmentación 2D revisado de Bone-Pores para mejorar el análisis óseo de micro-CT.
  • Introducir la interfaz de software BONe DL (BONe DLFit, BONe DLPred, BONe IoU) para gestionar conjuntos de datos grandes y diversos.
  • Evaluar la robustez del modelo, el rendimiento en diferentes arquitecturas y la reproducibilidad multiplataforma.

Principales métodos:

  • Se entrenó BP-2D-03 con un conjunto de datos de 20 escaneos de micro-CT de cinco especies de mamíferos (142.960 parches de imagen).
  • Se utilizó una interfaz de software de DL con módulos para entrenamiento, predicción y evaluación.
  • Se realizaron validación cruzada de 5 pliegues, experimentos de referencia sobre arquitectura/tamaño de parche y pruebas de consistencia multiplataforma.

Principales resultados:

  • BP-2D-03 demostró una Intersección sobre Unión (IoU) media estable y alta en todas las semillas, con alguna variación para escaneos atípicos.
  • Las arquitecturas U-Net y UNet++ con esqueletos convolucionales simples lograron valores de IoU cercanos a 0.97.
  • La segmentación de resultados fue consistente en diferentes plataformas de hardware, sistemas operativos e implementaciones de software.

Conclusiones:

  • El software BONe DL proporciona una línea de base sólida para la segmentación ósea en datos de micro-CT.
  • Las herramientas desarrolladas abordan limitaciones previas en el entrenamiento e implementación de modelos de DL.
  • El modelo y el software garantizan una segmentación ósea automatizada reproducible y confiable en diversas aplicaciones.