AIMC Topic: Circadian Rhythm

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New bridging eco-acoustic indices inspired by deep neural networks for fine-grained bird vocalization recognition across diurnal cycles.

PloS one
Revealing difference in bird vocalization changes from the perspectives of song recognition and acoustic indices has become a hot topic and challenge in recent ecological landscape research. This paper proposes a fine-grained (Dawn, noon, night) bird...

Total solar eclipse triggers dawn behavior in birds: Insights from acoustic recordings and community science.

Science (New York, N.Y.)
On 8 April 2024, a total solar eclipse disrupted light-dark cycles for North American birds during the lead-up to spring reproduction. Compiling more than 10,000 community observations and artificial intelligence analyses of nearly 100,000 vocalizati...

Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock.

Nature communications
The circadian clock of plants contributes to their survival and fitness. However, understanding clock function at the transcriptome level and its response to the environment requires assaying across high resolution time-course experiments. Generating...

Detection and Analysis of Circadian Biomarkers for Metabolic Syndrome Using Wearable Data: Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Wearable devices are increasingly used for monitoring health and detecting digital biomarkers related to chronic diseases such as metabolic syndrome (MetS). Although circadian rhythm disturbances are known to contribute to MetS, few studi...

Can circadian rhythms of heart rate variability identify major depressive disorder? - A study based on support vector machine analysis.

Asian journal of psychiatry
BACKGROUND: Major depressive disorder (MDD) is a prevalent and severe psychiatric condition for which objective diagnostic tools are lacking. Heart rate variability (HRV), an index of autonomic nervous system (ANS) function, has shown potential for d...

Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data.

Journal of biological rhythms
Accurately capturing the temporal distribution of polysomnographic sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyo...

Predictors of Sleep Latency From the Multiple Sleep Latency Test: A Random Forest Investigation in a Community Sample.

Journal of sleep research
This study aimed to advance the understanding of factors that predict mean sleep latency (MSL) on the multiple sleep latency test (MSLT) by applying machine learning methodology on a high-dimensional dataset from a large community sample. A cross-sec...

Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.

Journal of psychiatric research
Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is unclear. Given that depression disrupts circadian rhythms, assessing HRV fluctuations may better capt...

Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions.

Computers in biology and medicine
The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, ...

Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Predicting 24-hour intraocular pressure (IOP) fluctuations is crucial for enhancing glaucoma management. Traditional methods of measuring 24-hour IOP fluctuations are complex and present certain limitations. The present study leverages mac...