Some Tools to Analyze Changes of Rhythms in Biological Time Series

Hassnaoui, M.; Pupier, René; Attia, J.; Blanc, Alain; Beauchaud, Marilyn; Buisson, Bernard

doi:10.1076/brhm.29.4.353.1431

Cited by 6 publications

(3 citation statements)

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“…Triggering activity data was graphed as two dimensional plots to display the time series of individual demand activity in relation to time of day for the high-triggering fish in each experiment. The period length of rhythmic activity was determined by the concordogram method at a confidence level of 99 % (Hassnaoui et al, 1998). Period was calculated using data on the whole population, including high and low-triggering fish, as well as on the high-triggering ones alone.…”

Section: Discussionmentioning

confidence: 99%

Long-term monitoring of individual fish triggering activity on a self-feeding system: An example using European sea bass (Dicentrarchus labrax)

et al. 2006

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In two experiments, a computerized on-demand feeding system coupled with a PIT tag monitoring device was used to continuously record the triggering activity by ca. 50 individual sea bass for 55 days (Exp. 1, initial average body weight and coefficient of variation, CV: 299 g, 15%) and 69 days (Exp. 2, 157 g, 13%). Each group was stocked in 1 m3 tanks and exposed to a water temperature of 21 ± 1 °C and a light regime of 16:8 LD. Only 5% of the triggering activity was not associated with simultaneous PIT tag detection. Although each individual was detected via PIT tag during the experiment, only 67% and 74% of the fish actuated the trigger at least once in Exp. 1 and Exp. 2, respectively. Moreover, only two fish in Exp. 1 and one fish in Exp. 2 accounted for 82% (43 + 39) and 77% of the total triggering activity, respectively. These three, high-triggering individuals did not exhibit a higher specific growth rate or agonistic behavior as observed by video monitoring. Indeed, zero-triggering fish had either a slightly higher SGR (Exp. 1) or a higher final body weight (Exp. 2) compared with low-and high-triggering fish.

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Section: Discussionmentioning

confidence: 99%

Long-term monitoring of individual fish triggering activity on a self-feeding system: An example using European sea bass (Dicentrarchus labrax)

et al. 2006

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“…33 34 A one way analysis of variance (ANOVA) was used to test grouped data for the effect of time. Each series was analysed for circadian rhythmicity by the least squares method of fitting of cosines (single cosinor method 33 34 ) with periods in the expanded circadian range 14-34 hours, 41 and with 0.1 hours between trial periods. Group rhythm characteristics at precisely 2400 were summarised by population mean cosinor analysis.…”

Section: Methodsmentioning

confidence: 99%

Rhythmic variations in pain, stiffness, and manual dexterity in hand osteoarthritis

Bellamy¹

2002

Annals of the Rheumatic Diseases

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Objective: To explore circadian variation in pain, stiffness, and manual dexterity in patients with hand osteoarthritis (OA). Methods: Twenty one patients with hand OA, as defined by ACR criteria (17 women, four men, mean age 62.2 years, range 52-74 years) self rated pain and stiffness on separate 10 cm horizontal visual analogue scales and performed bead intubation coordinometry (BIC) six times each day (on waking up, at bedtime, and every four hours in between) for 10 consecutive days. Each series (using data with the trend removed if there was a significant trend) was analysed for circadian rhythmicity by a cosine vector technique (single cosinor). With individual data expressed as the percentage of the mean, group rhythm characteristics at period 24 hours were summarised for each variable by population mean cosinor analysis. Results: Individual analyses identified significant circadian rhythms at p<0.05 for pain (n=15/21), stiffness (n=16/20), and dexterity (n=18/21), and a significant circadian rhythm on a group basis was identified for pain (p=0.013), stiffness (p<0.001), and dexterity (p<0.001). Pain was least at 1610 and stiffness at 1618. Peak dexterity occurred in mid-afternoon at 1548 and occurred within the 95% confidence interval of least pain (1312-1800) and stiffness (1520-1732). Conclusions: Dexterity was influenced by the patient's level of pain or stiffness, which changed systematically throughout the day. Similar results have been previously reported in 14 patients with rheumatoid arthritis where peak dexterity occurred at 1544 and at 1528 in 14 age and sex matched healthy controls. The predictability of rhythmic variation in pain, stiffness, and dexterity has implications for scheduling activities of daily living and for timing antirheumatic drug treatment.

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“…The term circadian rhythm refers to biological rhythmical periods completed within 24 hours (in the range of 20 to 28 hours or, exceptionally, even 13 to 34 hours) 3,4) . The precise and reliable assessment of those periods was carried out by Czeisler et al 1) in 24 healthy young and older human subjects under controlled conditions.…”

Section: Discussionmentioning

confidence: 99%

Circadian Biorhythmicity in Normal Pressure Hydrocephalus - A Case Series Report

Herbowski¹

2022

J Korean Neurosurg Soc

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Continuous monitoring of intracranial pressure is a well established medical procedure. Still, little is known about long-term behavior of intracranial pressure in normal pressure hydrocephalus. The present study is designed to evaluate periodicity of intracranial pressure over long-time scales using intraventricular pressure monitoring in patients with normal pressure hydrocephalus. In addition, the circadian and diurnal patterns of blood pressure and body temperature in those patients are studied. Four patients, selected with “probable” normal pressure hydrocephalus, were monitored for several dozen hours. Intracranial pressure, blood pressure, and body temperature were recorded hourly. Autocorrelation functions were calculated and cross-correlation analysis were carried out to study all the time-series data. Autocorrelation results show that intracranial pressure, blood pressure, and body temperature values follow bimodal (positive and negative) curves over a day. The cross-correlation functions demonstrate causal relationships between intracranial pressure, blood pressure, and body temperature. The results show that long-term fluctuations in intracranial pressure exhibit cyclical patterns with periods of about 24 hours. Continuous intracranial pressure recording in “probable” normal pressure hydrocephalus patients reveals circadian fluctuations not related to the day and night cycle. These fluctuations are causally related to changes in blood pressure and body temperature. The present study reveals the complete loss of the diurnal blood pressure and body temperature rhythmicities in patients with “probable” normal pressure hydrocephalus.

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Some Tools to Analyze Changes of Rhythms in Biological Time Series

Cited by 6 publications

References 0 publications

Long-term monitoring of individual fish triggering activity on a self-feeding system: An example using European sea bass (Dicentrarchus labrax)

Long-term monitoring of individual fish triggering activity on a self-feeding system: An example using European sea bass (Dicentrarchus labrax)

Rhythmic variations in pain, stiffness, and manual dexterity in hand osteoarthritis

Circadian Biorhythmicity in Normal Pressure Hydrocephalus - A Case Series Report

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