Internal Clocks: Chronobiologists have discovered that thousands of genes and physiological processes—such as core body temperature, hormone levels, blood pressure, and alertness—follow a dual oscillation, peaking and dipping every 12 hours rather than just once a day. Biological "Rush Hours": This 12-hour cycle creates secondary peaks of metabolic and genetic activity around dawn and dusk, helping the body manage daily shifts in energy demands.The Post-Lunch Dip: The minor drop in alertness and body temperature that humans typically feel about 12 hours after the middle of the night (usually early-to-mid afternoon) is a direct reflection of this internal rhythm interacting with our sleep-wake cycle. Midday Naps in Past and Traditional Societies.

The Siesta Tradition: Across Mediterranean, Middle Eastern, Latin American, and parts of Asian history, a post-midday rest or siesta was standard practice.Beating the Heat: Societies naturally paused work during the hottest hours of the afternoon when ambient temperatures peaked and physical labor became inefficient or dangerous. Biphasic Sleep: This habit forms part of a biphasic sleep pattern (sleeping in two distinct daily blocks, such as a shortened night plus a daytime nap), which was widely practiced before industrial work schedules enforced a single, continuous 8-hour block of night sleep.

If you've spent any time in cycles research, you've run into the standard story of biological rhythms: every organism carries its own internal clock, a self-winding mechanism ticking away independent of the outside world. Through the 1950s and 60s, one Northwestern University biologist quietly built a career-long case that this story was, at minimum, incomplete.

Frank A. Brown Jr., Morrison Professor of Biological Sciences at Northwestern and a Consultant at the Marine Biological Laboratory in Woods Hole, wasn't a fringe figure. He was Vice President of the American Society of Zoologists, President of the Society of General Physiologists, and — worth noting for this community — an elected Director of the Foundation for the Study of Cycles from 1960 onward. His papers ran in PNAS, Biological Bulletin, and the American Journal of Physiology. And his experiments were genuinely strange.

The fiddler crab. Brown's earliest work showed that fiddler crabs, which darken and lighten daily via pigment movement, kept perfect 24-hour rhythm even sealed in total darkness at 26°C, 16°C, or 6°C — as if something outside the box was still reaching them.

The potato that predicted pressure. This is the one that made the New York Times (April 17, 1959). Brown sealed pieces of potato — no nervous system, so no behavioral tricks possible — in hermetically closed, constant-condition chambers and tracked their oxygen consumption continuously for over two years. Two results stood out: a lunar-monthly rhythm in metabolic rate (lowest at new moon, peaking at third quarter), and — stranger still — a metabolic rate that tracked barometric pressure roughly two days before that pressure actually arrived. Salamanders in a parallel study showed the same anticipatory pattern.

The oysters that moved their tides inland. Maybe the best story of all. Brown took oysters from New Haven, Connecticut — where they naturally open their shells at high tide — and shipped them to landlocked Evanston, Illinois. Over time, the oysters shifted their opening schedule to match the high tide Evanston would have, if Evanston were on the coast. No tide, no obvious cue — just a slow drift toward a rhythm keyed to a force nobody could point to directly.

Magnetism, into the '60s. By 1962, working with geologist Karl C. Hammer, Brown reported that snails, worms, and the single-celled paramecium could detect magnetic fields, and that the planarian worm Dugesia even distinguished north from south poles. By 1964 he was writing directly for Cycles Magazine on the topic ("How Animals Respond To Magnetism"), and the Foundation was floating the idea that regular fluctuations in the earth's magnetic field might be a hidden driver behind some of the cycles the FSC had been cataloguing for years.

The jet-lag note. Almost in passing, Cycles (March 1963) cited Brown's observation that a person flown from California to England keeps their body clock — waking pattern, temperature, blood cell counts, hormones — running on California time for eight to ten days before it resets. This is, essentially, an early clinical description of jet lag, years before the term existed.

Brown's proposed mechanism — that organisms are entrained by very weak geophysical signals like barometric pressure, cosmic radiation, and magnetism, rather than running on a purely self-contained clock — lost out to the now-dominant endogenous-clock model in chronobiology. But he wasn't wrong that something was going on in his data, and he was never a crank: credentialed, published, elected. For anyone interested in the roads not taken in cycle science, he's a genuinely interesting figure to dig into further.

Sourced from Cycles Magazine, 1959–1964 (Vols. 10–15), Foundation for the Study of Cycles.

First published: 2020

Brief summary

Reference overview classifying biological rhythms by period length -- ultradian (seconds to hours, e.g. cell division, REM sleep cycles), circadian (~24 hours), and infradian (days to years) -- with the mathematical definitions used in chronobiology (MESOR, amplitude, acrophase).

Article

Biological Rhythm Overview: Ultradian, Circadian and Infradian Classification is a reference overview published by ScienceDirect Topics in 2020. It provides a reference overview that classifies biological rhythms by period length -- ultradian (seconds to hours, e.g. cell division, REM sleep cycles), circadian (~24 hours), and infradian (days to years) -- with the mathematical definitions used in chronobiology (MESOR, amplitude, acrophase).

The analysis focuses on ultradian (seconds-hours), circadian (~24h), infradian (days-years). It also considers defines period as 1/frequency and gives the standard chronobiology terminology (MESOR, amplitude, acrophase). This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.

The article reports the following result: Rhythmicity can be observed over the entire range of time scales from fractions of a second to years, but can be broadly grouped into three categories: ultradian, circadian, and infradian, with the period equal to 1/frequency. The interpretation is strongest when sample selection, age, physiology, measurement error and individual variability are accounted for.

For cycles researchers, the article brings together biological rhythms, ultradian rhythms, circadian rhythms, infradian rhythms. It is relevant to biological cycle research because living systems contain interacting clocks and rhythms whose periods vary with physiology, age, environment and measurement method.

Because this is a reference overview, it is best used as an accessible introduction or perspective rather than as conclusive evidence. Forum discussion should follow its references back to the primary data and original research wherever possible.

Source details and credits

• Source / publisher: ScienceDirect Topics
• Source type: Reference overview
• URL type: WWW
• Credits: ScienceDirect Topics
• URL: https://www.sciencedirect.com/topics/neuroscience/biological-rhythm

First published: 2020

Brief summary

Statistical modeling of a large real-world menstrual cycle dataset examines the relationship between total cycle length and the timing of ovulation, refining prediction of the fertile window beyond the traditional fixed-cycle assumption.

Article

Real-Life Insights on Menstrual Cycles and Ovulation Using Big Data is a peer-reviewed journal article published by Human Reproduction Open (Oxford) in 2020. It focuses on statistical modelling of a large real-world menstrual cycle dataset examines the relationship between total cycle length and the timing of ovulation, refining prediction of the fertile window beyond the traditional fixed-cycle assumption.

The analysis focuses on cycle lengths analysed across 23-35 day range. It also considers robust regression modelling of follicular phase vs. total cycle length. It also considers large real-world tracking-app dataset. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.

The article reports the following result: The current study investigated a large dataset on the menstrual cycles of women seeking to conceive, using robust regression analysis to explore the relationship between the follicular phase and total cycle length. The interpretation is strongest when sample selection, age, physiology, measurement error and individual variability are accounted for.

For cycles researchers, the article brings together menstrual cycles, ovulation timing, fertile window, reproductive big data. It is relevant to biological cycle research because living systems contain interacting clocks and rhythms whose periods vary with physiology, age, environment and measurement method.

Because it is a peer-reviewed journal article, the article is a strong starting point for discussion in the Biology & Human Biology forum, although its conclusions should still be compared with later replications and updated datasets.

Source details and credits

• Source / publisher: Human Reproduction Open (Oxford)
• Source type: Peer-reviewed journal article
• URL type: WWW
• Credits: Human Reproduction Open (Oxford)
• URL: https://academic.oup.com/hropen/article/2020/2/hoaa011/5820371

First published: 2019

Brief summary

Large-scale analysis of over 600,000 ovulatory cycles from a fertility-tracking app finds a mean cycle length of 29.3 days, with mean follicular phase of 16.9 days and luteal phase of 12.4 days, and documents how cycle length shortens with age.

Article

Real-World Menstrual Cycle Characteristics of More Than 600,000 Menstrual Cycles is a peer-reviewed journal article published by npj Digital Medicine (Nature) in 2019. It is a large-scale analysis of over 600,000 ovulatory cycles from a fertility-tracking app that finds a mean cycle length of 29.3 days, with mean follicular phase of 16.9 days and luteal phase of 12.4 days, and documents how cycle length shortens with age.

The analysis focuses on mean 29.3 days (infradian). It also considers follicular phase 16.9 days, luteal phase 12.4 days. The data source is 612,613 cycles from 124,648 users of the Natural Cycles app. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.

The authors analyse 612,613 ovulatory cycles with a mean length of 29.3 days from 124,648 users. Mean cycle length decreased by 0.18 days per year of age from 25 to 45 years. The interpretation is strongest when sample selection, age, physiology, measurement error and individual variability are accounted for.

For cycles researchers, the article brings together menstrual cycle length, ovulation, follicular phase, fertility tracking. It is relevant to biological cycle research because living systems contain interacting clocks and rhythms whose periods vary with physiology, age, environment and measurement method.

Because it is a peer-reviewed journal article, the article is a strong starting point for discussion in the Biology & Human Biology forum, although its conclusions should still be compared with later replications and updated datasets.

Source details and credits

• Source / publisher: npj Digital Medicine (Nature)
• Source type: Peer-reviewed journal article
• URL type: WWW
• Credits: npj Digital Medicine (Nature)
• URL: https://www.nature.com/articles/s41746-019-0152-7

First published: 2017

Brief summary

Finds that cultured Drosophila cells lacking the recognized circadian clock genes still generate genuine ~24-hour oscillations in gene transcription and metabolism, indicating an uncharacterized clock-independent mechanism for daily biological rhythms.

Article

Non-Canonical Circadian Oscillations in Drosophila S2 Cells Drive Gene-Expression Cycles Coupled to Metabolic Oscillations is a preprint published by bioRxiv in 2017. It finds that cultured Drosophila cells lacking the recognised circadian clock genes still generate genuine ~24-hour oscillations in gene transcription and metabolism, indicating an uncharacterized clock-independent mechanism for daily biological rhythms.

The analysis focuses on ~24 hours, measured via flow cytometry and proteomic time-course sampling over two days. It also considers explicitly shown to be independent of canonical circadian clock genes and the cell division cycle. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.

The article reports the following result: These results suggest that an uncharacterised mechanism, independent of canonical circadian genes or the cell cycle, is involved in the generation of 24-hour transcriptional oscillations in Drosophila S2 cells. The interpretation is strongest when sample selection, age, physiology, measurement error and individual variability are accounted for.

For cycles researchers, the article brings together drosophila circadian rhythms, gene-expression cycles, metabolic oscillations, biological clocks. It is relevant to biological cycle research because living systems contain interacting clocks and rhythms whose periods vary with physiology, age, environment and measurement method.

Because it is a preprint, the work should be read alongside later peer-reviewed publications and independent replications. It remains useful because the proposed cycle, dataset and analytical approach are stated clearly enough to be scrutinised.

Source details and credits

• Source / publisher: bioRxiv
• Source type: Preprint
• URL type: PDF
• Credits: bioRxiv
• URL: https://www.biorxiv.org/content/10.1101/191338.full.pdf

Article

Sleep is a recurring biological state characterised by reduced interaction with the environment, altered consciousness, distinctive brain activity, and changes in muscle tone, breathing, circulation, hormone release, and metabolism.

Human sleep is organised into non-rapid-eye-movement sleep and rapid-eye-movement sleep. These stages recur in ultradian cycles during the night, while the timing of sleep is regulated by interaction between circadian rhythms and a homeostatic pressure that increases during wakefulness.

Sleep contributes to memory, learning, emotional regulation, immune function, metabolic regulation, tissue repair, and brain maintenance. The exact functions of sleep remain an active area of research and may differ among sleep stages and species.

Sleep duration and architecture vary with age, health, environment, behaviour, and individual biology. Sleep deprivation, circadian disruption, breathing disorders, insomnia, movement disorders, and neurological or psychiatric conditions can impair sleep quality and daytime function.

Source details and credits

• Source / publisher: Wikipedia
• URL type: WWW
• Credits: Wikipedia
• URL: https://en.wikipedia.org/wiki/Sleep

Article

Sjögren's syndrome is a chronic autoimmune disease in which immune activity commonly damages the glands that produce tears and saliva. The characteristic symptoms are persistent dry eyes and dry mouth, although the condition can affect many other organs.

Inflammation may involve the joints, skin, lungs, kidneys, nerves, blood vessels, and digestive or reproductive systems. Fatigue and pain are common, and symptoms vary greatly among individuals.

Diagnosis may use symptom history, examination, tear and salivary measurements, blood tests for autoantibodies, imaging, and sometimes a minor salivary-gland biopsy. No single finding is present in every affected person.

Sjögren's syndrome can occur alone or with another autoimmune disorder such as rheumatoid arthritis or lupus. Management focuses on relieving dryness, protecting eyes and teeth, treating systemic inflammation when present, and monitoring complications, including an increased risk of some lymphomas.

Source details and credits

• Source / publisher: Wikipedia
• URL type: WWW
• Credits: Wikipedia
• URL: https://en.wikipedia.org/wiki/Sjo%CC%88gren's_syndrome

Article

Electroencephalography, or EEG, records voltage fluctuations at the scalp that arise mainly from synchronised electrical activity in populations of cortical neurons. The recording is made through electrodes placed at standardised positions on the head.

EEG signals contain rhythms over a range of frequencies, commonly described using bands such as delta, theta, alpha, beta, and gamma. These patterns vary with sleep, wakefulness, attention, sensory input, movement, development, medication, and neurological conditions.

Clinical EEG is used in the assessment of epilepsy, altered consciousness, sleep disorders, encephalopathy, brain injury, and other conditions. Event-related potentials are obtained by averaging EEG responses that are time-locked to a stimulus or action.

EEG has excellent temporal resolution but relatively limited spatial precision because electrical signals are mixed and filtered by the brain, skull, and scalp. Interpretation requires attention to artefacts from eyes, muscles, movement, equipment, and the surrounding electrical environment.

Source details and credits

• Source / publisher: Wikipedia
• URL type: WWW
• Credits: Wikipedia
• URL: https://en.wikipedia.org/wiki/Electroencephalography