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

Protein Folding

Protein Folding

Protein Folding Quality Check in the RER

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...

You might also read

Related Articles

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

Sort by

Same author

Smartphone-Based Postoperative Wound Assessment Following Laparoscopic Surgery in a Resource-Limited Setting: A Prospective Cohort Study.

Bioengineering (Basel, Switzerland)·2026

Same author

A novel lightweight deep learning model for early prediction of cardiovascular disease.

Frontiers in artificial intelligence·2026

Same author

Enhancing products performance evaluation through hybrid DistilRoBERTa and BiGRU models.

PloS one·2026

Same author

Residents' Research Experiences and Barriers: A Qualitative Study to Identify Strategies for Research Skill Enhancement among Residents in a Medical Teaching Institution.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP·2026

Same author

Improving personalized recommendations system using graph attention networks driven by perceived complexity and innovation.

Scientific reports·2026

Same author

Towards intelligent edge computing through reinforcement learning based offloading in public edge as a service.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique.

Hamid Masood Khan¹, Fazal Masud Khan¹, Aurangzeb Khan²

¹Institute of Computing and Information Technology, Gomal University, D.I.Khan, Pakistan.

Computational and Mathematical Methods in Medicine

|April 1, 2021

Summary

This summary is machine-generated.

This research introduces a novel framework for anomaly detection using Hierarchical Temporal Memory (HTM) and Semantic Folding Theory (SFT). The model continuously learns temporal sequences and identifies deviations, adapting to new patterns as normal behavior.

More Related Videos

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Related Experiment Videos

Last Updated: Nov 10, 2025

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Artificial Intelligence
Neuroscience
Machine Learning

Background:

The human neocortex inspires the Hierarchical Temporal Memory (HTM) model for sequence learning.
Semantic Folding Theory (SFT) utilizes HTM to represent data streams as sparse distributed representations (SDRs).
SFT provides a structural basis for semantic representation in natural language processing.

Purpose of the Study:

To develop a robust anomalous behavior detection framework using HTM-based SFT.
To improve decision-making through anomaly detection in data streams.
To enable continuous learning of temporal sequences and adapt to evolving patterns.

Main Methods:

Implementation of Hierarchical Temporal Memory (HTM) for sequence learning.
Application of Semantic Folding Theory (SFT) for data stream representation using SDRs.
Development of an unsupervised learning rule for continuous temporal sequence analysis.

Main Results:

The proposed framework effectively detects static (spatial) and temporal anomalies.
The HTM system demonstrates continuous learning, adapting to new patterns as 'normal'.
The model learns the order of variables in temporal sequences through unsupervised learning.

Conclusions:

The HTM-based SFT framework offers a robust approach to anomalous behavior detection.
This method enhances decision-making by accurately identifying deviations in data streams.
The system's continuous learning capability allows it to adapt to dynamic data environments.