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

Membrane Fluidity01:23

Membrane Fluidity

168.1K
Cell membranes are composed of phospholipids, proteins, and carbohydrates loosely attached to one another through chemical interactions. Molecules are generally able to move about in the plane of the membrane, giving the membrane its flexible nature called fluidity. Two other features of the membrane contribute to membrane fluidity: the chemical structure of the phospholipids and the presence of cholesterol in the membrane.
168.1K
Membrane Fluidity01:26

Membrane Fluidity

13.8K
Membrane fluidity is explained by the fluid mosaic model of the cell membrane, which describes the plasma membrane structure as a mosaic of components—including phospholipids, cholesterol, proteins, and carbohydrates—that gives the membrane a fluid character.
Mosaic nature of the membrane
The mosaic characteristic of the membrane helps the plasma membrane remain fluid. The integral proteins and lipids exist as separate but loosely-attached molecules in the membrane. The membrane is...
13.8K
Detergent Purification of Membrane Proteins01:18

Detergent Purification of Membrane Proteins

5.9K
Detergents are used to purify the integral proteins of the membrane. The hydrophobic portion of the detergent can replace membrane phospholipids while solubilizing the membrane proteins. When detergent monomers reach a specific concentration in a solution called critical micelle concentration (CMC), they form micelles. Above CMC, the concentration of the detergent monomers remains in equilibrium with the micelle. The number of detergent monomers present in the CMC varies for each detergent, and...
5.9K

You might also read

Related Articles

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

Sort by
Same author

A comprehensive survey of data-driven technologies for construction solid waste recycling systems.

Waste management (New York, N.Y.)·2026
Same author

Multi-batch treatment of swine slaughterhouse wastewater by microalgal-fungal consortium system: Stability and effects on dissolved organic matter.

Environmental research·2026
Same author

Ginkgolide A enhances cardiomyocyte differentiation from pluripotent stem cells by targeting cytochrome c to attenuate intrinsic apoptosis.

The Journal of biological chemistry·2026
Same author

Targeted silencing of CLYBL with platelet-mimetic siRNA nanoparticles drives itaconate-mediated macrophage reprogramming and protects against sepsis-triggered lung cell death.

Cell death discovery·2026
Same author

New insights into effect of PBAT microplastics on latosol microbial metabolic functions.

Journal of hazardous materials·2026
Same author

Multi-omics reveals a novel Cxcr4<sup>+</sup> subpopulation of alveolar macrophages and therapeutic effect of AMD3100 in mice with advanced silicosis.

Clinical and translational medicine·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Nov 22, 2025

Experimental Multiscale Methodology for Predicting Material Fouling Resistance
09:13

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

1.5K

Data-Driven Intelligent Warning Method for Membrane Fouling.

Xiaolong Wu, Honggui Han, Junfei Qiao

    IEEE Transactions on Neural Networks and Learning Systems
    |January 8, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an intelligent warning system to predict membrane fouling in membrane bioreactors (MBRs). The method uses a recurrent fuzzy neural network (RFNN) and state comprehensive evaluation (SCE) for accurate wastewater treatment process monitoring.

    More Related Videos

    Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing
    10:19

    Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing

    Published on: February 13, 2016

    11.6K
    Detection of Detergent-sensitive Interactions Between Membrane Proteins
    10:09

    Detection of Detergent-sensitive Interactions Between Membrane Proteins

    Published on: March 7, 2018

    6.1K

    Related Experiment Videos

    Last Updated: Nov 22, 2025

    Experimental Multiscale Methodology for Predicting Material Fouling Resistance
    09:13

    Experimental Multiscale Methodology for Predicting Material Fouling Resistance

    1.5K
    Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing
    10:19

    Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing

    Published on: February 13, 2016

    11.6K
    Detection of Detergent-sensitive Interactions Between Membrane Proteins
    10:09

    Detection of Detergent-sensitive Interactions Between Membrane Proteins

    Published on: March 7, 2018

    6.1K

    Area of Science:

    • Environmental Engineering
    • Artificial Intelligence in Water Treatment

    Background:

    • Membrane fouling poses a significant operational challenge in membrane bioreactors (MBRs), potentially disrupting wastewater treatment processes (WWTPs).
    • Effective monitoring and early warning systems are crucial for maintaining the efficiency and reliability of MBR operations.

    Purpose of the Study:

    • To design a data-driven intelligent warning method for predicting future membrane fouling events in MBRs.
    • To develop a robust system capable of real-time monitoring and proactive alerts for wastewater treatment facilities.

    Main Methods:

    • Implementation of a soft-computing model utilizing a recurrent fuzzy neural network (RFNN) for real-time membrane permeability identification.
    • Development of a multistep prediction strategy to accurately forecast membrane permeability over extended horizons, minimizing error accumulation.
    • Integration of a state comprehensive evaluation (SCE) method for assessing MBR pollution levels and triggering warnings.

    Main Results:

    • The proposed recurrent fuzzy neural network (RFNN) model accurately identifies real-time membrane permeability.
    • The multistep prediction strategy effectively reduces error accumulation for accurate long-term forecasting.
    • The state comprehensive evaluation (SCE) method successfully evaluates pollution levels, enabling timely warnings.
    • Experimental validation in real-world plants confirmed the proposed method's efficiency and effectiveness in predicting and warning against membrane fouling.

    Conclusions:

    • The developed data-driven intelligent warning system, integrating RFNN and SCE, offers a reliable solution for predicting and mitigating membrane fouling in MBRs.
    • This approach enhances the operational stability and efficiency of wastewater treatment processes by providing early warnings.
    • The system's successful field testing demonstrates its practical applicability and benefits for WWTP management.