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

Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...
Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...
Chronopharmacokinetics: Circadian Rhythms and Influence on Drug Response01:15

Chronopharmacokinetics: Circadian Rhythms and Influence on Drug Response

Circadian rhythms are cyclic changes that are crucial in plasma drug concentrations. Various standard circadian parameters, including core body temperature, heart rate, and other cardiovascular factors, directly impact disease states and the therapeutic response to drug therapy.
The time of drug administration is an important factor to consider, as it can influence the toxic dose of a drug. For example, a study conducted by Prins et al. in 1997 examined the effects of the timing of...
Biological Clocks and Seasonal Responses02:45

Biological Clocks and Seasonal Responses

The circadian—or biological—clock is an intrinsic, timekeeping, molecular mechanism that allows plants to coordinate physiological activities over 24-hour cycles called circadian rhythms. Photoperiodism is a collective term for the biological responses of plants to variations in the relative lengths of dark and light periods. The period of light-exposure is called the photoperiod.
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

A transcriptional program associated with neurotransmission in the living human brain.

Molecular psychiatry·2026
Same author

Neural signatures and personalized neuromodulation in a subject experiencing context-dependent inhibitory control deficits.

medRxiv : the preprint server for health sciences·2025
Same author

Rest-activity rhythm phenotypes in adults with epilepsy and intellectual disability.

Epilepsia open·2025
Same author

Bayesian covariate-dependent graph learning with a dual group spike-and-slab prior.

Biometrics·2025
Same author

Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

Biometrics·2024
Same author

Bayesian network-guided sparse regression with flexible varying effects.

Biometrics·2024
Same journal

A Bayesian Time-Varying Psychophysiological Interaction Model.

Data science in science·2026
Same journal

Neurodatascience: Past, Present, and Future.

Data science in science·2026
Same journal

A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.

Data science in science·2026
Same journal

Enhancing Health Research with Machine Learning: Practical Case Studies Using the <i>All of Us</i> Researcher Workbench.

Data science in science·2025
Same journal

TIME-VARYING <i>ℓ</i> <sub>0</sub> OPTIMIZATION FOR SPIKE INFERENCE FROM MULTI-TRIAL CALCIUM RECORDINGS.

Data science in science·2025
Same journal

Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-derived Shape and Intensity Features.

Data science in science·2025
See all related articles

Related Experiment Video

Updated: Jul 4, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms.

Beniamino Hadj-Amar1, Vaishnav Krishnan2, Marina Vannucci1

  • 1Department of Statistics, Rice University, Houston, TX, USA.

Data Science in Science
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model to analyze sleep-activity patterns from wearable devices. It helps understand how factors like demographics and health influence rest-activity rhythms in epilepsy patients.

Keywords:
Anti-logistic Circadian modell1-ball projection priormulti-subject modelingrest-activity rhythmswereable devices

More Related Videos

A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
10:16

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

Related Experiment Videos

Last Updated: Jul 4, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
10:16

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

Area of Science:

  • Biostatistics
  • Chronobiology
  • Wearable Technology

Background:

  • Rest-activity rhythms are crucial for health.
  • Analyzing circadian data from wearables presents challenges.
  • Existing models may lack flexibility in incorporating individual factors.

Purpose of the Study:

  • To develop a flexible Bayesian model for analyzing wearable-derived circadian activity data.
  • To integrate covariate effects on amplitude and phase of circadian rhythms.
  • To promote model sparsity and identify significant predictors using an l1-ball projection prior.

Main Methods:

  • Proposed a Bayesian covariate-dependent anti-logistic circadian model.
  • Integrated covariates into amplitude and phase parameter modeling.
  • Employed an l1-ball projection prior for model sparsity.
  • Validated the model using simulated and real-world actigraphy data from epilepsy patients.

Main Results:

  • The model effectively analyzes circadian activity data from wearable devices.
  • Demonstrated the ability to uncover complex relationships influencing rest-activity rhythms.
  • Identified significant demographic, psychological, and medical factors affecting circadian patterns.
  • Showcased enhanced flexibility and interpretability in cohort-level analysis.

Conclusions:

  • The proposed Bayesian model offers a powerful tool for analyzing wearable-based circadian data.
  • Provides valuable insights into factors affecting rest-activity rhythms, particularly in clinical populations like epilepsy.
  • Facilitates personalized clinical assessments and targeted healthcare interventions based on individual circadian profiles.