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Videos de Conceptos Relacionados

Improving Translational Accuracy02:07

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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...
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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Scaling01:26

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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Language and Cognition01:27

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Video Experimental Relacionado

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Perfiles de interés científico escalables utilizando modelos de lenguaje grandes

Yilun Liang1,2, Gongbo Zhang1, Edward Sun3

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, USA.

ArXiv
|September 2, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Los grandes modelos de lenguaje (LLM) pueden automatizar el perfil de interés científico. Los perfiles generados utilizando los términos de Medical Subject Headings (MeSH) mostraron una mejor legibilidad y se prefirieron a los perfiles basados en resúmenes abstractos, a pesar de las diferencias con los resúmenes escritos por humanos.

Palabras clave:
Divergencia de Kullback y LeiblerGrandes modelos de lenguajeGeneración del lenguaje naturalPerfiles de los investigadores

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Área de la Ciencia:

  • Informática biomédica
  • La inteligencia artificial en la investigación
  • Comunicación Científica

Sus antecedentes:

  • Los perfiles de investigación de los científicos son cruciales para el descubrimiento y la colaboración de talentos, pero a menudo están desactualizados.
  • Se necesitan métodos automatizados y escalables para mantener los perfiles de investigación actuales.

Objetivo del estudio:

  • Diseñar y evaluar métodos basados en grandes modelos lingüísticos para la generación de perfiles de interés científico.
  • Comparar perfiles generados por máquinas (de resúmenes de PubMed y términos de MeSH) con los intereses auto-resumidos de los investigadores.

Principales métodos:

  • Utilizó GPT-4o-mini para resumir los intereses de investigación de 595 miembros de la facultad en función de sus resúmenes de PubMed y términos de MeSH.
  • Datos recopilados de publicaciones (títulos, términos de MeSH, resúmenes) de la facultad de CUIMC.
  • Evaluaciones realizadas manualmente y automáticamente para comparar los perfiles generados por máquina y los escritos por uno mismo.

Principales resultados:

  • La superposición léxica fue baja entre los perfiles generados por máquina y los escritos por uno mismo (bajas puntuaciones ROUGE-L, BLEU, METEOR).
  • Se encontró una similitud semántica moderada utilizando BERTScore (F1: 0.542 basado en MeSH, 0.555 basado en abstracto).
  • Las revisiones manuales favorecieron los perfiles basados en MeSH (67,86%) para la legibilidad (93,44%) y la impresión general (77,78% buena / excelente).

Conclusiones:

  • Los LLM ofrecen una solución escalable para automatizar el perfil de interés científico.
  • Los perfiles derivados del término MeSH demuestran una mayor legibilidad y preferencia del usuario en comparación con los perfiles derivados del término abstracto.
  • Los perfiles generados por máquinas difieren en la elección del concepto de los escritos por humanos, destacando el potencial para la generación de ideas novedosas en los perfiles manuales.