Jove
Visualize
Contact Us
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 Concept Videos

Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
Nucleophilic Aromatic Substitution: Addition–Elimination (SNAr)01:30

Nucleophilic Aromatic Substitution: Addition–Elimination (SNAr)

Nucleophilic substitution in aromatic compounds is feasible in substrates bearing strong electron-withdrawing substituents positioned ortho or para to the leaving group. The reaction proceeds via two steps: the addition of the nucleophile and the elimination of the leaving group.
The reaction begins with an attack of the nucleophile on the carbon that holds the leaving group. This results in the delocalization of the π electrons over the ring carbons. The resonance interaction between the...
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Quantifying Work02:30

Quantifying Work

As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.

You might also read

Related Articles

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

Sort by
Same author

Free-Breathing 3D Whole Heart and Aorta Cine MRI Without Contrast Agent-Comparison to Clinical Standard.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Radiology AI Lab: Evaluation of Radiology Applications with Clinical End-Users.

Journal of imaging informatics in medicine·2025
Same author

Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT.

Journal of imaging informatics in medicine·2025
Same author

A Novel Radiology Communication Tool to Reduce Workflow Interruptions: Clinical Evaluation of RadConnect.

Journal of imaging informatics in medicine·2024
Same author

Design and Perceived Value of a Novel Solution for Asynchronous Communication in Radiology.

Current problems in diagnostic radiology·2023
Same author

A PACS-Integrated Tool to Automatically Extract Patient History From Prior Radiology Reports.

Journal of the American College of Radiology : JACR·2016

Related Experiment Video

Updated: Jun 4, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

SNOMED CT Saves Keystrokes: Quantifying Semantic Autocompletion.

Merlijn Sevenster1, Zharko Aleksovski

  • 1Philips Research, Eindhoven, The Netherlands.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

Adding semantic context to autocompletion algorithms significantly improves performance. This enhancement, using SNOMED CT terms, can save up to 18% more keystrokes for users.

Related Experiment Videos

Last Updated: Jun 4, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Area of Science:

  • Medical Informatics
  • Computer Science
  • Natural Language Processing

Background:

  • Autocompletion enhances user experience and adherence to standardized terminology.
  • Contextual information is intuitively linked to improved autocompletion accuracy.
  • Current autocompletion algorithms can be further optimized with richer contextual data.

Purpose of the Study:

  • To quantify the added value of contextual information for autocompletion algorithms.
  • To measure performance improvement in terms of average saved keystrokes.
  • To investigate the impact of semantic relationships on autocompletion efficiency.

Main Methods:

  • Representing user context as a set of SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) terms.
  • Utilizing the SNOMED CT ontology to calculate semantic distances between terms.
  • Integrating a semantic distance function into autocompletion algorithms to prioritize contextually relevant suggestions.

Main Results:

  • Semantic enhancement of autocompletion algorithms demonstrated a significant reduction in keystrokes.
  • Up to 18% additional keystrokes were saved compared to non-semantic base algorithms.
  • The study confirmed the benefit of incorporating semantic proximity into autocompletion.

Conclusions:

  • Semantic context integration offers a substantial improvement for autocompletion systems.
  • The use of SNOMED CT provides a robust framework for semantic autocompletion.
  • This approach enhances efficiency and user interaction in applications requiring standardized terminology.