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

Superconductor01:24

Superconductor

1.9K
A substance that reaches superconductivity, a state in which magnetic fields cannot penetrate, and there is no electrical resistance, is referred to as a superconductor. In 1911, Heike Kamerlingh Onnes of Leiden University, a Dutch physicist, observed a relation between the temperature and the resistance of the element mercury. The mercury sample was then cooled in liquid helium to study the linear dependence of resistance on temperature. It was observed that, as the temperature decreased, the...
1.9K
Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K
Types Of Superconductors01:28

Types Of Superconductors

1.7K
A superconductor is a substance that offers zero resistance to the electric current when it drops below a critical temperature. Zero resistance is not the only interesting phenomenon as materials reach their transition temperatures. A second effect is the exclusion of magnetic fields. This is known as the Meissner effect. A light, permanent magnet placed over a superconducting sample will levitate in a stable position above the superconductor. High-speed trains that levitate on strong...
1.7K
Semiconductors01:22

Semiconductors

1.8K
There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
1.8K
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

2.6K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area vector...
2.6K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.3K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2026
Same author

Leveraging large language models for structured information extraction from pathology reports.

Journal of pathology informatics·2025
Same author

Monitoring sleep duration, timing, and continuity among US youth and adults in NHANES using actigraphy.

Sleep health·2025
Same author

mSigSDK - private computation of mutation signatures.

Research square·2025
Same author

Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.

bioRxiv : the preprint server for biology·2025
Same author

WorkflowHub: a registry for computational workflows.

Scientific data·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 28, 2026

Polysome Purification from Soybean Symbiotic Nodules
07:02

Polysome Purification from Soybean Symbiotic Nodules

Published on: July 1, 2022

1.4K

QMachine: commodity supercomputing in web browsers.

Sean R Wilkinson1, Jonas S Almeida

  • 1Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, USA. wilkinson@uab.edu.

BMC Bioinformatics
|June 11, 2014
PubMed
Summary
This summary is machine-generated.

QMachine (QM) leverages web browsers for distributed scientific computing, enabling scalable bioinformatics analysis without local software. This approach addresses privacy concerns in biomedical research by utilizing volunteer machines for computation.

More Related Videos

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

9.1K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

2.0K

Related Experiment Videos

Last Updated: Apr 28, 2026

Polysome Purification from Soybean Symbiotic Nodules
07:02

Polysome Purification from Soybean Symbiotic Nodules

Published on: July 1, 2022

1.4K
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

9.1K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

2.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Distributed Computing

Background:

  • Cloud computing advances offer new possibilities for scientific computing and distributed workflows.
  • Web browsers can function as high-performance workstations for genomics data analysis, eliminating the need for local software installation.
  • QMachine (QM) was designed to integrate this pervasive computing model with the Web of Linked Data in Biomedicine.

Purpose of the Study:

  • To introduce QMachine (QM), an open-sourced web service for distributed scientific computing.
  • To demonstrate QM's capability in analyzing large-scale genomics data using volunteer computing resources.
  • To highlight QM's potential for scalable bioinformatics solutions and addressing privacy concerns.

Main Methods:

  • QM operates as an open-sourced, publicly available web service using HTTP for task submission and result retrieval.
  • It utilizes a distributed model where volunteer machines execute analytical and data retrieval tasks.
  • A client library offers high-level distribution templates, including MapReduce.

Main Results:

  • QM successfully distributed the analysis of 20 Streptococcus pneumoniae genomes.
  • The system requires minimal server resources as tasks are executed by volunteer machines.
  • QM has garnered significant community interest, receiving over 2.2 million API calls from 87 countries in 12 months.

Conclusions:

  • QM is suitable for delivering scalable bioinformatics solutions for computation- and data-intensive workflows.
  • The sandboxed execution of code in web browsers enhances security and addresses privacy concerns in biomedical research.
  • QM represents a departure from traditional reliance on expensive server hardware and "download and install" software.