Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

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...

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

How Data are Classified: Numerical Data

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...

Associative Learning

Associative Learning

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.
Classical conditioning, also known...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

You might also read

Related Articles

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

Sort by

Same author

Viral Pathogen-Specific Severe Acute Respiratory Infections in Hospitalised Adults in Europe: Epidemiological Findings from the id.DRIVE Study 2024-2025.

Infectious diseases and therapy·2026

Same author

Metabolomic signatures of SSRI exposure during neural differentiation and correlation of lysophosphatidylcholines with early symptoms of neurodevelopmental disorders.

EBioMedicine·2026

Same author

Stratifying the shoreline: a modified OSPAR framework to monitor event-driven beach litter.

Environmental monitoring and assessment·2026

Same author

Multiplexed Tandem Mass Spectrometry Imaging Enables Large-Scale Isomer Mapping and Annotation in Tissues.

Angewandte Chemie (International ed. in English)·2026

Same author

Monitoring antimicrobial resistance trends and emerging carbapenemases in Enterobacterales causing companion animal infections: a four-year study.

Veterinary microbiology·2026

Same author

Durability of the BNT162b2 XBB:1.5-adapted vaccine against JN.1 hospitalisation in Europe, October 2023 to August 2024: A test-negative case-control study using the id.DRIVE platform.

PloS one·2026

Search research articles

Related Experiment Video

Updated: Feb 14, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform.

Felix Friedrich¹, Cátia Marques¹, Ingela Lanekoff^1,2

¹Department of Chemistry for Life Sciences, Uppsala University, Uppsala 75 123, Sweden.

Analytical Chemistry

|February 13, 2026

Summary

Single-cell metabolomics (SCM) reveals cellular differences. A new MATLAB tool, CellMate, simplifies SCM data analysis, enabling deeper insights into metabolite heterogeneity.

More Related Videos

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Feb 14, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Biochemistry
Analytical Chemistry
Computational Biology

Background:

Single-cell metabolomics (SCM) is crucial for understanding cellular heterogeneity.
Existing SCM data analysis tools are limited and often incompatible with conventional methods.
High-resolving mass spectrometry enables sensitive detection of metabolites in single cells.

Purpose of the Study:

To introduce CellMate, a MATLAB-based platform for processing single-cell metabolomics data.
To provide a user-friendly interface for metabolite identification and peak alignment.
To support quantitative, targeted, and untargeted metabolomic workflows.

Main Methods:

Development of CellMate, a MATLAB platform utilizing direct infusion techniques.
Implementation of a graphical user interface for intuitive data processing.
Integration of a deep learning algorithm for distinguishing metabolites in untargeted workflows.

Main Results:

CellMate facilitates metabolite identification and peak alignment for SCM data.
The platform supports customizable quantitative, targeted, and untargeted metabolomic analyses.
A deep learning model effectively differentiates endogenous metabolites from background noise.

Conclusions:

CellMate enhances the analysis of single-cell metabolomics data, overcoming current limitations.
The tool enables comprehensive extraction of metabolite information from individual cells.
CellMate advances the capabilities of the single-cell metabolomics research toolbox.