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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.6K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.6K
Intelligence01:27

Intelligence

8.7K
The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
8.7K
Measures of Intelligence01:29

Measures of Intelligence

8.5K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
8.5K
Membrane Domains01:18

Membrane Domains

7.2K
The membrane domains concentrate specific lipids and proteins at one place within the membrane, which helps in cell signaling, adhesion, and other critical cellular processes. These domains can differ in size, composition, function, and lifespan.
Protein Domains
The membrane comprises a group of distinct proteins responsible for carrying out a cell's specific function. For example, the plasma membrane of the human sperm, or a single germ cell, contains a unique set of proteins in the...
7.2K
Three Developmental Domains01:29

Three Developmental Domains

1.1K
Human development is typically examined across three main domains: physical, cognitive, and socio-emotional. These domains represent the significant areas of change and continuity throughout the lifespan, from infancy to late adulthood.
Physical Development
Physical processes, also known as maturation, encompass the biological changes that occur across an individual's life. These changes begin with genetic inheritance and continue through various stages, including growth in height and weight,...
1.1K
Three-Domain System of Life01:21

Three-Domain System of Life

1.3K
Ribosomal RNA (rRNA) sequence analysis revealed three distinct groups of cells: eukaryotes, bacteria, and archaea. In 1978, Carl R. Woese proposed the concept of domains, a taxonomic level above kingdoms, to differentiate these groups. He suggested that archaea and bacteria, despite their similar appearance, represent separate domains. Domains differ in rRNA, membrane lipid structure, transfer RNA, and antibiotic sensitivity.In this classification, animals, plants, and fungi belong to the...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Adipose Tissue Inflammation, Oxidative Stress, and Altered Adipogenesis Are Associated With Dyslipidemia in Obesity: A Multiomics Profiling Study.

Journal of the American Heart Association·2026
Same author

10 × Genomics Flex Gene Expression is a powerful tool for single-cell transcriptomics of xenografts models.

Genome biology·2026
Same author

Lactic acid bacteria and endogenous ethanol mediate proton pump inhibitor-associated MASLD: a multicohort cross-sectional mediation analysis.

Gut microbes·2026
Same author

Peripheral blood DNA methylation signatures to predict treatment response in Crohn's disease - Authors' reply.

The lancet. Gastroenterology & hepatology·2025
Same author

An exploratory single-cell analysis of peripheral blood mononuclear cells from vedolizumab-treated Crohn's disease patients identifies response-associated differences among the plasmacytoid dendritic cells and classical monocytes.

Frontiers in immunology·2025
Same author

Application of image guided analyses to monitor fecal microbial composition and diversity in a human cohort.

Scientific reports·2025

Related Experiment Video

Updated: Feb 8, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.4K

Domain intelligible models.

Sultan Imangaliyev1, Andrei Prodan1, Max Nieuwdorp2

  • 1Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; Horaizon BV, 2625 GZ Delft, The Netherlands.

Methods (San Diego, Calif.)
|July 8, 2018
PubMed
Summary
This summary is machine-generated.

New algorithms leverage group relationships in metagenomic data for improved biological insights. This approach aids in predicting responses to fecal microbiota transplantation (FMT) and understanding insulin sensitivity.

More Related Videos

One Dimensional Turing-Like Handshake Test for Motor Intelligence
14:05

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

28.5K
Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees
14:31

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Published on: July 15, 2009

14.5K

Related Experiment Videos

Last Updated: Feb 8, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.4K
One Dimensional Turing-Like Handshake Test for Motor Intelligence
14:05

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

28.5K
Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees
14:31

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Published on: July 15, 2009

14.5K

Area of Science:

  • Computational biology
  • Metagenomics
  • Machine learning

Background:

  • Organizing '-omics' data features into biologically relevant groups enhances information mining.
  • Phylogenetic trees exemplify grouping based on genetic or physical similarities, aiding biological interpretation.

Purpose of the Study:

  • To develop algorithms for integrating auxiliary group information into metagenome learning.
  • To build interpretable models by guiding feature selection with predictor relationships.

Main Methods:

  • Developed cost functions that use auxiliary information to ensure related predictor groups contribute similarly.
  • Incorporated group relationships and predictor interdependencies into the metagenome learning task.

Main Results:

  • Successfully applied algorithms to a fecal microbiota transplantation (FMT) dataset.
  • Identified factors associated with improved peripheral insulin sensitivity post-FMT.
  • Achieved accurate predictions of FMT response.

Conclusions:

  • The developed algorithms effectively integrate auxiliary biological information for enhanced metagenome analysis.
  • This approach improves the interpretability and predictive accuracy of models for clinical outcomes like insulin sensitivity.