Jove
Visualize
Contact Us

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
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...
14.0K
Lateralization01:28

Lateralization

949
Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
949
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.5K
3.5K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.7K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.7K
Lagging Strand Synthesis01:59

Lagging Strand Synthesis

60.8K
During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...
60.8K

You might also read

Related Articles

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

Sort by
Same journal

Effects of audio guided loving kindness meditation on psychological well being and laboratory stress responsiveness in healthy university students.

Scientific reports·2026
Same journal

Adaptive cognitive driven cross modal network for few shot fine grained recognition.

Scientific reports·2026
Same journal

Tegoprazan-based dual therapy versus bismuth-containing quadruple therapy for Helicobacter pylori eradication: a prospective, multicenter, open-label, non-inferiority, randomized controlled trial.

Scientific reports·2026
Same journal

Primary tumor resection prior to peptide receptor radionuclide therapy is associated with improved survival in metastatic gastroenteropancreatic neuroendocrine tumors: a systematic review and meta-analysis.

Scientific reports·2026
Same journal

Sleep duration among medical students and its association with bronchial asthma, anxiety, and depression.

Scientific reports·2026
Same journal

MGMT deficiency augments STING-mediated inflammatory responses accompanied by metabolic alterations in macrophages.

Scientific reports·2026
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 Experiment Video

Updated: Jan 11, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

LLMs outperform outsourced human coders on complex textual analysis.

Vicente J Bermejo1, Andrés Gago2, Ramiro H Gálvez2

  • 1ESADE Business School, Universitat Ramon Llull, Barcelona, 08034, Spain. vicente.bermejo@esade.edu.

Scientific Reports
|November 17, 2025
PubMed
Summary

Large language models (LLMs) show superior performance over human coders in extracting complex information from Spanish news articles. This technology offers a cost-effective solution for sophisticated text analysis, even for non-programmers.

Related Experiment Videos

Last Updated: Jan 11, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Sophisticated text analysis is crucial for extracting complex information from large datasets.
  • Traditional methods often rely on manual coding, which can be time-consuming and expensive.
  • Large language models (LLMs) have emerged as powerful tools for text processing.

Purpose of the Study:

  • To evaluate the effectiveness of LLMs in extracting complex information from text data.
  • To compare the performance of various LLMs against outsourced human coders.
  • To assess LLM accuracy across diverse natural language processing tasks.

Main Methods:

  • Utilized a corpus of Spanish news articles.
  • Compared LLM performance with outsourced human coders on five NLP tasks.
  • Tasks included named entity recognition and identifying political criticism.

Main Results:

  • LLMs consistently outperformed outsourced human coders.
  • LLM superiority was most pronounced in tasks demanding deep contextual understanding.
  • Accuracy in reproducing expert annotations was higher with LLMs.

Conclusions:

  • Current LLM technology provides a viable and cost-effective alternative for text analysis.
  • Researchers without programming expertise can leverage LLMs for sophisticated text analysis.
  • LLMs demonstrate significant potential in advancing natural language processing applications.