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

Neural Control of Respiration01:18

Neural Control of Respiration

2.4K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
2.4K
Oxygen Delivering System II: Venturi Mask and Transtracheal Oxygen01:16

Oxygen Delivering System II: Venturi Mask and Transtracheal Oxygen

571
Oxygen therapy is a pivotal aspect of medical care, particularly for patients with respiratory ailments. Two prominent oxygen-delivering systems include the Venturi mask and the transtracheal oxygen catheter.
Venturi Mask
The Venturi mask, named after the Venturi effect, is designed to deliver precise oxygen concentrations. It consists of a large tube with an oxygen inlet that narrows down, causing a pressure drop that pulls air in through adjustable side ports. The mask is a lightweight,...
571
Breathing01:05

Breathing

59.2K
The process of breathing, inhaling and exhaling, involves the coordinated movement of the chest wall, the lungs, and the muscles that move them. Two muscle groups with important roles in breathing are the diaphragm, located directly below the lungs, and the intercostal muscles, which lie between the ribs. When the diaphragm contracts, it moves downward, increasing the volume of the thoracic cavity and creating more room for the lungs to expand. When the intercostal muscles contract, the ribs...
59.2K
Mechanical Ventilation III: Noninvasive Ventilation01:23

Mechanical Ventilation III: Noninvasive Ventilation

112
Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
Noninvasive Positive-Pressure Ventilation...
112
Assessment of Respiration01:23

Assessment of Respiration

1.1K
The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
1.1K
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

1.5K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
1.5K

You might also read

Related Articles

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

Sort by
Same author

Systems Pharmacology and Pharmaco-Transcriptomic Insights into Bauhinia variegata for Polycystic Ovary Syndrome Management.

Current pharmaceutical design·2026
Same author

Facile synthesis of zinc-coordinated lignin nanoparticles derived from sugarcane bagasse waste biomass for sustainable bioactive composites.

Discover nano·2026
Same author

Modeling soil water distribution under drip fertigation in chrysanthemum across soil types using HYDRUS-2D.

Scientific reports·2026
Same author

Brain white matter microstructural integrity differences between pre-post buprenorphine-naloxone treated individuals with opioid use disorder: Evidence from a DTI study.

Psychiatry research. Neuroimaging·2026
Same author

Update and reuse: Structure-guided nanobody evolution against SARS-CoV-2 escape.

PLoS pathogens·2026
Same author

An Ascending Aortic Pseudoaneurysm: Anesthetic Challenges.

Annals of cardiac anaesthesia·2026

Related Experiment Video

Updated: Jul 1, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

4.0K

BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer.

Abhishek Verma1, Virender Ranga2, Dinesh Kumar Vishwakarma3

  • 1Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi, 110042, India. abhishekcms08@gmail.com.

Environmental Monitoring and Assessment
|March 4, 2024
PubMed
Summary

This study introduces BREATH-Net, a novel deep learning model using satellite and ground data to accurately predict nitrogen dioxide (NO2) levels in Delhi. The model significantly improves air quality monitoring and forecasting, aiding in mitigating respiratory health issues.

Keywords:
Air pollutionAir quality managementDeep learningDelhiLSTMMachine learningNitrogen dioxidePredictionSatellite dataTransformerUrban areas

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

1.0K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jul 1, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

4.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

1.0K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Environmental Science
  • Data Science
  • Artificial Intelligence

Background:

  • Air pollution, particularly nitrogen dioxide (NO2), significantly impacts urban health, exacerbating respiratory and cardiovascular conditions.
  • Accurate monitoring and prediction of NO2 levels are crucial for developing effective urban air quality management strategies.
  • Existing methods may lack the precision needed for real-time, localized air quality assessment in densely populated areas.

Purpose of the Study:

  • To develop and evaluate a novel hybrid deep learning model (BREATH-Net) for predicting nitrogen dioxide (NO2) concentrations in Delhi.
  • To leverage satellite data from Sentinel 5P and ground-based monitoring data for enhanced air quality forecasting.
  • To contribute to mitigating respiratory health issues associated with urban air pollution through advanced data-driven methodologies.

Main Methods:

  • Gathering and analyzing 3 years of satellite (Sentinel 5P) and ground-based NO2 data for Delhi.
  • Employing exploratory data analysis (EDA) for pattern identification and data pre-processing with MinMaxScaler.
  • Developing a hybrid deep learning model, BREATH-Net, combining Transformer and BiLSTM architectures for time-series forecasting.

Main Results:

  • The proposed BREATH-Net model demonstrates high efficacy in predicting NO2 levels in Delhi.
  • The study reports a significant improvement in prediction accuracy with a Root Mean Square Error (RMSE) of 9.06 compared to state-of-the-art models.
  • The hybrid architecture effectively captures both short-term dependencies (BiLSTM) and long-range relationships (Transformer) in the sequential data.

Conclusions:

  • BREATH-Net offers a powerful and accurate approach for monitoring and forecasting urban NO2 pollution.
  • The successful implementation of this model can substantially enhance urban air quality control strategies.
  • This research highlights the potential of integrating satellite data with deep learning for effective environmental health management.