Chronobiology is the scientific field focused on studying periodicity in biological processes. In mammals, most physiological variables exhibit circadian rhythmicity, such as metabolism, body ...temperature, locomotor activity, and sleep. The biological rhythmicity can be statistically evaluated by examining the time series and extracting parameters that correlate to the period of oscillation, its amplitude, phase displacement, and overall variability.
We have developed a library called CircadiPy, which encapsulates methods for chronobiological analysis and data inspection, serving as an open-access toolkit for the analysis and interpretation of chronobiological data. The package was designed to be flexible, comprehensive and scalable in order to assist research dealing with processes affected or influenced by rhythmicity.
The results demonstrate the toolkit's capability to guide users in analyzing chronobiological data collected from various recording sources, while also providing precise parameters related to the circadian rhythmicity.
The analysis methodology from this proposed library offers an opportunity to inspect and obtain chronobiological parameters in a straightforward and cost-free manner, in contrast to commercial tools.
Moreover, being an open-source tool, it empowers the community with the opportunity to contribute with new functions, analysis methods, and graphical visualizations given the simplified computational method of time series data analysis using an easy and comprehensive pipeline within a single Python object.
•Toolkit with a modular approach for analyzing chronobiological data•The library proved to be efficient for different types of input data•The parameters obtained were compatible with those obtained by other methods•The library is open-source and can be improved with input from the scientific community
Cosinor-based rhythmometry Cornelissen, Germaine
Theoretical biology and medical modelling,
2014-Apr-11, 2014-4-11, 20140411, Letnik:
11, Številka:
1
Journal Article
Recenzirano
Odprti dostop
A brief overview is provided of cosinor-based techniques for the analysis of time series in chronobiology. Conceived as a regression problem, the method is applicable to non-equidistant data, a major ...advantage. Another dividend is the feasibility of deriving confidence intervals for parameters of rhythmic components of known periods, readily drawn from the least squares procedure, stressing the importance of prior (external) information. Originally developed for the analysis of short and sparse data series, the extended cosinor has been further developed for the analysis of long time series, focusing both on rhythm detection and parameter estimation. Attention is given to the assumptions underlying the use of the cosinor and ways to determine whether they are satisfied. In particular, ways of dealing with non-stationary data are presented. Examples illustrate the use of the different cosinor-based methods, extending their application from the study of circadian rhythms to the mapping of broad time structures (chronomes).
Data processing in analysis of circadian rhythm was performed in various ways. However, there was a lack of evidence for the optimal analysis of circadian rest-activity rhythm. Therefore, we aimed to ...perform mathematical simulations of data processing to investigate proper evidence for the optimal analysis of circadian rest-activity rhythm.
Locomotor activities of 20 ICR male mice were measured by infrared motion detectors. The data of locomotor activities was processed using data summation, data average, and data moving average methods for cosinor analysis. Circadian indices were estimated according to time block, respectively. Also, statistical F and p-values were calculated by zero-amplitude test.
The data moving average result showed well-fitted cosine curves independent of data processing time. Meanwhile, the amplitude, MESOR, and acrophase were properly estimated within 800 seconds in data summation and data average methods.
These findings suggest that data moving average would be an optimal method for data processing in a cosinor analysis and data average within 800-second data processing time might be adaptable. The results of this study can be helpful to analyze circadian restactivity rhythms and integrate the results of the studies using different data processing methods.
Catatonia is a neuropsychiatric syndrome associated with both psychiatric disorders and medical conditions. Understanding of the pathophysiology of catatonia remains limited, and the role of the ...environment is unclear. Although seasonal variations have been shown for many of the disorders underlying catatonia, the seasonality of this syndrome has not yet been adequately explored.
Clinical records were screened to identify a cohort of patients suffering from catatonia and a control group of psychiatric inpatients, from 2007 to 2016 in South London. In a cohort study, the seasonality of presentation was explored fitting regression models with harmonic terms, while the effect of season of birth on subsequent development of catatonia was analyzed using regression models for count data. In a case-control study, the association between month of birth and catatonia was studied fitting logistic regression models.
In total, 955 patients suffering from catatonia and 23,409 controls were included. The number of catatonic episodes increased during winter, with a peak in February. Similarly, an increasing number of cases was observed during summer, with a second peak in August. However, no evidence for an association between month of birth and catatonia was found.
The presentation of catatonia showed seasonal variation in accordance with patterns described for many of the disorders underlying catatonia, such as mood disorders and infections. We found no evidence for an association between season of birth and risk of developing catatonia. This may imply that recent triggers may underpin catatonia, rather than distal events.
GPS collars are a technology that is used extensively to monitor livestock due to its versatility. In this study, the main objective was to confirm whether they can detect the circadian rhythmicity ...that modulates the behavior of free-grazing sheep. The Churra-breed flock that was monitored grazed an approx. 166-ha fenced area within a dehesa ecosystem in the northwestern Iberian Peninsula. Geolocations were recorded every 30 min for two years. Animal activities were categorized based on the speed; an animal was “moving” if the speed was > 0 m/s (the analyzed category), and “resting” if the speed was 0 m/s. Sheep grazing activity in terms of their speed, azimuth, and distance traveled, was subjected to a circadian adjustment derived from the online Cosinor tool. Results reveal that the flock activity, whether based on speed, distance traveled, or azimuth, fit a circadian rhythmicity (p < 0.05). In the summer, particularly July and August, sheep exhibited a significant advance in the acrophase (the time at which the peak of a rhythm occurs), which might have been caused by day length and temperature. In all seasons, flock activity was significantly higher in the diurnal period, while the lowest activity was found in all cases at night, although in the summer sheep activity was high at dawn. In addition, in the day, sheep activity was significantly higher in the fall than it was at other times of the year. The preferred grazing direction of the sheep was non-random, since it was modulated by the contour orientation and the limits of the grazing area. It could be concluded that GPS geolocations allow to demonstrate that free-grazing sheep activity is modulated by a circadian rhythmicity.
•GPS devices detect sheep behavioural patterns in grazing activity.•Sheep grazing activity is modulated by a circadian rhythmicity.•Sheep follow spatial patterns related to the topography.•A preferred grazing direction that coincides with contour orientation was found.•Sheep might follow a “cognitive map” of the grazing area.
Circadian rhythms of physiology, behavior, and metabolism have an endogenous 24 h period that synchronizes with environmental cycles of light/dark and food availability. Alterations in light cycles ...are stressful and disrupt such diurnal oscillations. Recently, we witnessed a sudden rise in studies describing the mechanisms behind the interaction between the key characteristics of mitochondrial functions, peripheral clocks, and stress responses. To our knowledge, there is no study in the suprachiasmatic nuclei (SCN) describing the dysregulated mitochondrial bioenergetics under abnormal lighting conditions, which is common in today's modern world. Thus, we aimed to investigate the existence of daily changes in mitochondrial bioenergetics (respiratory control rate, RCR), mitochondrial abundance (mtDNA/nDNA), plasma corticosterone, and to test whether disturbances in the lighting conditions might influence such rhythms. To confirm this, mice were sacrificed, mitochondria were isolated from the suprachiasmatic nuclei in the brain and blood was collected, every 3 h at various time points zeitgeber time/circadian time, (0, 3, 6, 9, 12, 15, 18, 21, and 24 h) under 12:12 h light-dark (LD, 150 lux L: 0 lux D) cycle and chronic artificial dim lighting (LL, 5 lux: 5lux) conditions, of a 24 h period, respectively. Our results demonstrate the existence of robust daily rhythmicity in RCR, mtDNA/nDNA and plasma CORT under a normal LD cycle. However, these rhythms were significantly disrupted and clock genes expressions were dysregulated under chronic dim LL. Furthermore, mitochondrial abundance was significantly reduced during LL compared to their numbers under LD cycle. Our data demonstrate that the circadian clock regulates mitochondrial functions (RCR, number), essential for accomplishing daily energy demands and supply by the SCN neurons. Abnormal light exposure dysregulates mitochondrial functions in the SCN and may alter metabolism, resulting in obesity, diabetes, and other metabolic disorders. Therefore, properly designing lighting conditions in workplaces is essential to mitigate the adverse consequences of light on humans.
Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has ...several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Intracranial high-frequency oscillations (HFOs) show a significant diurnal rhythm.•Diurnal rhythm of HFOs is relatively attenuated within the seizure-onset zone (SOZ).•Peak difference in HFO density ...within/outside SOZ consistently precedes seizures.•Difference in HFO density within/outside SOZ peaks at 1st hour after arousal and ±2 hours around sleep onset.
Seizures are known to occur with diurnal and other rhythms. To gain insight into the neurophysiology of periodicity of seizures, we tested the hypothesis that intracranial high-frequency oscillations (HFOs) show diurnal rhythms and sleep-wake cycle variation. We further hypothesized that HFOs have different rhythms within and outside the seizure-onset zone (SOZ).
In drug-resistant epilepsy patients undergoing stereotactic-EEG (SEEG) monitoring to localize SOZ, we analyzed the number of 50-200 Hz HFOs/channel/minute (HFO density) through a 24-hour period. The distribution of HFO density during the 24-hour period as a function of the clock time was analyzed with cosinor model, and for non-uniformity with the sleep-wake cycle.
HFO density showed a significant diurnal rhythm overall and both within and outside SOZ. This diurnal rhythm of HFO density showed significantly lower amplitude and longer acrophase within SOZ compared to outside SOZ. The peaks of difference in HFO density within and outside SOZ preceded the seizures by approximately 4 hours. The difference in HFO density within and outside SOZ also showed a non-uniform distribution as a function of sleep-wake cycle, with peaks at first hour after arousal and ±2 hours around sleep onset.
Our study shows that the diurnal rhythm of intracranial HFOs is more robust outside the SOZ. This suggests cortical tissue within SOZ generates HFOs relatively more uniformly throughout the day with attenuation of expected diurnal rhythm. The difference in HFO density within and outside SOZ also showed non-uniform distribution according to clock times and the sleep-wake cycle, which can be a potential biomarker for preferential times of pathological cortical excitability. A temporal correlation with seizure occurrence further substantiates this hypothesis.
A temporal and geographical analysis of echolocation activity can give insights into the behaviour of free-ranging harbour porpoises Phocoena phocoena. Seasonal and diel patterns in the presence and ...foraging activity of harbour porpoises were investigated based on a year-long passive acoustic monitoring data set recorded at 5 sites in the western Baltic Sea. Diel patterns in detection rates were found at 4 sites. A year-round rhythm in presence, however, was found at only 1 station, whereas the other 3 stations showed diel rhythms for 2 to 3 seasons. Three of the sites showed diel patterns in foraging sequences on a seasonal level, but no station showed such patterns for the complete year of investigation. Both diurnal and nocturnal patterns in harbour porpoise detections were observed, indicating that diel rhythmic behaviour is more complex than previously reported. In contrast, foraging behaviour showed only nocturnal rhythms. Owing to the limitations in passive acoustic monitoring, all categorized foraging sequences are a minimum estimate. Therefore, classified foraging sequences are most likely pelagic foraging, while bottom grubbing could have been missed. Differences in the occurrence of foraging sequences between station, season and time of day lead to the assumption that the long-term echolocation diel patterns of porpoises strongly depend on the temporal changes in food availability and composition within a certain habitat. Echolocation behaviour of foraging porpoises is strongly influenced by seasonally available prey resources, which require adaptive foraging strategies. Therefore, owing to seasonal variations, analyses of diel patterns need to be conducted over sufficiently long time periods and large geographic scales to allow generalized interpretation of the findings. Consequently, no general conclusion regarding diel rhythms in harbour porpoise echolocation was found. We hypothesize that porpoises in the study area alternate between foraging on benthic prey in shallow waters at daytime and in the pelagic in deeper waters at night.
PDF
