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fast_asleep/Cellstatetransitions_34_17.txt | ama, M., Sandaltzopoulou, E., Villava, C. E., Takata, N., Eiraku, M. and Ebisuya, M. (2021). Optogenetic control of apical constriction induces synthetic morphogenesis in mammalian tissues. bioRxiv. https://doi.org/10.1101/2021.04.20.440475
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Masaki, H., Kato-Itoh, M., Takahashi, Y., Umino, A., Sato, H., I... |
fast_asleep/full_45_0.txt | Acknowledgments
We would like to thank the sleep laboratory members (Teruaki Nochino, Shingo Haraki, and Akiko Tsujisaka) for their helpful advice on this manuscript and technicians (Kataoka N, Takahashi C, Nakamura M, Teshima Y, Maekawa T, Yamamoto A, Koda S, Hirai N, Iwaki A, Nishida M, and Nakamura U) for their tech... |
fast_asleep/3926054_32_0.txt | WASO percentage is relative to TRT–SOL. Stage N1/R percentages are relative to TST. Bold font highlights significant adjusted odds ratios. |
fast_asleep/full_156_0.txt | Trinder, J., Allen, N., Kleiman, J., Kralevski, V., Kleverlaan, D., Anson, K., et al. (2003). On the nature of cardiovascular activation at an arousal from sleep. Sleep 26, 543–551. doi: 10.1093/sleep/26.5.543 |
fast_asleep/3926054_16_1.txt | . |
fast_asleep/full_79_0.txt | PubMed Abstract | CrossRef Full Text | Google Scholar |
fast_asleep/Cellstatetransitions_36_10.txt |
Crossref (17)
Call for papers: Uncovering Developmental Diversity
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Development invites you to submit your latest research to our upcoming special issue: Uncovering Developmental Diversity. This issue will be coordinated by our academic Editor C... |
fast_asleep/full_164_0.txt | Žemaitytė, D., Varoneckas, G., and Sokolov, E. (1984). Heart rhythm control during sleep. Psychophysiology 21, 279–289. doi: 10.1111/j.1469-8986.1984.tb02935.x |
fast_asleep/full_126_0.txt | Rosipal, R., Lewandowski, A., and Dorffner, G. (2013). In search of objective components for sleep quality indexing in normal sleep. Biol. Psychol. 94, 210–220. doi: 10.1016/j.biopsycho.2013.05.014 |
fast_asleep/3926054_36_2.txt | which further increased the model fit significantly. Therefore, the transition probability from stage N2 to stage N1 was consistently among the most important features in ID–CTRL discrimination across samples. |
fast_asleep/Cellstatetransitions_25_0.txt | THE ROLE OF HETEROGENEITY/ASYNCHRONY
Although transitions have to be coordinated to ensure the right cells are generated at the right time and in the correct location, asynchrony, heterogeneity and noise might play a fundamental role in cell state changes. One of the key points debated in the themed discussion was the ... |
fast_asleep/full_116_0.txt | Moser, D., Kloesch, G., Fischmeister, F. P., Bauer, H., and Zeitlhofer, J. (2010). Cyclic alternating pattern and sleep quality in healthy subjects—is there a first-night effect on different approaches of sleep quality? Biol. Psychol. 83, 20–26. doi: 10.1016/J.BIOPSYCHO.2009.09.009 |
fast_asleep/Cellstatetransitions_12_0.txt | Discrete versus continuous cell states
Defining the state of a cell based on its constituents is already a challenge in terminally differentiated tissues or tissues with limited turnover (e.g. the cerebral cortex). In such systems, cells typically maintain stable patterns of gene expression, chromatin modifications, et... |
fast_asleep/full_76_0.txt | Hartmann, E. (1968). The 90-minute sleep-dream cycle. Arch. Gen. Psychiatry 18, 280–286. doi: 10.1001/archpsyc.1968.01740030024004 |
fast_asleep/full_3_0.txt | Table of contents
Abstract
Introduction
Materials and Methods
Results
Discussion
Conclusion
Data Availability Statement
Ethics Statement
Author Contributions
Funding
Conflict of Interest
Acknowledgments
References |
fast_asleep/rsta20140093_30_0.txt | (b) Spectral entropy measures
Kirsch et al. [11] proposed two spectral entropy measures specially suited for application to categorical time series: the WSE and the HSE. These spectral entropy measures can be interpreted as a quantification of the joint frequency distributions of all symbols contained in the original d... |
fast_asleep/3926054_2_0.txt | Results
People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in ... |
fast_asleep/rsta20140093_23_0.txt | Figure 2 shows transition matrices obtained by averaging the transition probabilities between different sleep stages in each of the four classes: Young/Normal, Young/Sleep Disorder, Old/Normal, and Old/Sleep Disorder. One can see that the cells next to the diagonal have highest probability which shows that usually tran... |
fast_asleep/3926054_6_0.txt | METHODS
Participants
The study was approved by the ethics committee of the VU University Medical Center, Amsterdam, The Netherlands. Participants were recruited through advertisement and the Sleep Registry20 and were screened by telephone followed by a face-to-face structured interview with a sleep specialist (MSc in p... |
fast_asleep/full_138_0.txt | Tamaki, M., Bang, J. W., Watanabe, T., and Sasaki, Y. (2016). Night watch in one brain hemisphere during sleep associated with the first-night effect in humans. Curr. Biol. 26, 1190–1194. doi: 10.1016/j.cub.2016.02.063 |
fast_asleep/Cellstatetransitions_0_1.txt | cell states
Environmental context: are cell states autonomous or non-autonomous?
Does the road cells take matter?
Transitions between states
From data to models
Perspectives
Acknowledgements
References
SPOTLIGHT| 19 OCTOBER 2021
Cell state transitions: definitions and challenges
Carla Mulas
ORCID logo
, Agathe Chaigne... |
fast_asleep/3926054_14_0.txt | Sleep Stage Bout Duration Distributions
Group-level bout duration distributions, first for sleep and wakefulness and subsequently for each separate sleep stage, were assessed with survival-analytical techniques, similar to those carried out in previous studies on sleep–wake transition patterns.26–29 To first investigat... |
fast_asleep/full_49_0.txt | Google Scholar |
fast_asleep/3926054_17_0.txt | For bout duration distributions, the within-group confidence intervals of the survival functions were estimated through a bootstrap approach.32 Group differences in the survival probabilities were determined by means of permutation Mann–Whitney tests.33 For both bootstrap and permutation, resampling was done over 10,00... |
fast_asleep/Cellstatetransitions_14_0.txt | Conversely, more recent analysis of transitions by single cell RNA-sequencing have suggested continuous transcriptional trajectories. These observations have led many scientists to rethink one of the oldest models of stem cell differentiation: the haematopoietic hierarchy (Laurenti and Göttgens, 2018). The observations... |
fast_asleep/3926054_50_7.txt | ; 114(9): 1715–1723.
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... |
fast_asleep/3926054_23_2.txt | .35 .02
WASO, minutes 29.0 ± 21.1 52.6 ± 42.8 1308 2.85 .004
Stage N1, minutes 20.4 ± 12.1 35.8 ± 23.3 1342 3.14 .002
Stage N2, minutes 186.8 ± 49.2 175.0 ± 53.8 812 –1.28 .20
Stage N3, minutes 113.9 ± 42.1 95.1 |
fast_asleep/rsta20140093_67_0.txt | Footnotes
One contribution of 12 to a theme issue ‘Enhancing dynamical signatures of complex systems through symbolic computation’. |
fast_asleep/rsta20140093_10_0.txt | — time in bed=time between intention to sleep/lights off and wake up/lights on, |
fast_asleep/3926054_6_3.txt | self-reported sleep as assessed by the ISI and the 7-day sleep diary. |
fast_asleep/Cellstatetransitions_31_0.txt | Finally, the type of data used to describe the system ultimately constrains the models we can use. For example, a Markov process assumes we know and can measure all the variables that influence cell states and transitions. However, this is generally not possible. Moreover, even if the key parameters are known, most bio... |
fast_asleep/3926054_10_1.txt | , 116, 150 of the HydroCel Geodesic Sensor Net, approximately equivalent to F3, F4, C3, C4, O1, O2, respectively, in the 10–20 system) and two electrooculogram leads (1 cm below the left and above the right outer canthi) referenced to linked mastoids and one bipolar chin electromyogram channel. Each 30-second epoch was... |
fast_asleep/3926054_49_1.txt | ScholarGoogle PreviewWorldCatCOPAC
4. Edinger JD Bonnet MH Bootzin RR et al. .; American Academy of Sleep Medicine Work Group. Derivation of research diagnostic criteria for insomnia: report of an American Academy of Sleep Medicine Work Group. Sleep. 2004; 27(8): 1567–1596.
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5. Gail... |
fast_asleep/rsta20140093_20_1.txt | percentage of wakefulness (W) during the sleep period steadily increases with age. However, the percentages of wakefulness in healthy subjects and patients with sleep disorders differ only slightly. Only for the oldest subjects were marked differences observed between normal subjects and patients with sleep disorders.... |
fast_asleep/full_44_0.txt | Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. |
fast_asleep/3926054_5_0.txt | Whereas to the best of our knowledge, sleep stage dynamics have not previously been investigated in ID, two studies have reported on the overall dynamics between sleep and wakefulness. One study18 showed that people with ID, in addition to having more frequent and longer nocturnal awakenings, wake from light sleep stag... |
fast_asleep/Cellstatetransitions_16_0.txt | Environmental context: are cell states autonomous or non-autonomous?
Whether the transitions in cell state are temporally continuous or discrete, they are also influenced by the context in which the cell is found. Indeed, the key parameters defining cell state can be cell-autonomous, but they can also be extrinsic, mod... |
fast_asleep/full_86_0.txt | Jurysta, F., Lanquart, J.-P., Sputaels, V., Dumont, M., Migeotte, P.-F., Leistedt, S., et al. (2009). The impact of chronic primary insomnia on the heart rate – EEG variability link. Clin. Neurophysiol. 120, 1054–1060. doi: 10.1016/j.clinph.2009.03.019 |
fast_asleep/full_25_0.txt | Table 2
www.frontiersin.org
Table 2. Mean continuity time for sleep and each sleep stage. |
fast_asleep/full_59_0.txt | CrossRef Full Text | Google Scholar |
fast_asleep/Cellstatetransitions_11_2.txt | allow for the combination of functional and molecular assays in single cells (Chen et al., 2021 preprint). |
fast_asleep/full_115_0.txt | CrossRef Full Text | Google Scholar |
fast_asleep/Cellstatetransitions_34_16.txt | Maki, K., Nava, M. M., Villeneuve, C., Chang, M., Furukawa, K. S., Ushida, T. and Wickström, S. A. (2021). Hydrostatic pressure prevents chondrocyte differentiation through heterochromatin remodeling. J. Cell Sci. 134, jcs247643. https://doi.org/10.1242/jcs.247643
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Martínez-Ara, G., Taberner, N., ... |
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Figure 3b and c shows comparisons of the distributions Normal versus Sleep Disorder and Young versus Old, respectively. The lower entropy values for subjects with sleep disorder (figure 3b) indicate lower complexity due to missing sleep transitions involving deep sleep. |
fast_asleep/full_53_0.txt | Google Scholar |
fast_asleep/3926054_22_0.txt | RESULTS
Table 2 summarizes the means and standard deviations of all conventional PSG sleep parameters for cases and controls in the Amsterdam sample, as well as the Wilcoxon rank-sum statistics and corresponding significance of group differences. As compared to CTRL, people with ID had significantly less TST, lower SE,... |
fast_asleep/full_48_0.txt | Agnew, H. W. J., Webb, W. B., and Williams, R. L. (1966). The first night effect: an EEG study of sleep. Psychophysiology 2, 263–266. |
fast_asleep/3926054_39_2.txt | large effect size of the transition probability from stage N2 to stage N1 observed in the Amsterdam sample involves an elevated number of arousals in people with ID.35,40 Different scoring criteria have also been shown to affect the scoring of REM sleep in young people but not in old people.38,39 The effect of this di... |
fast_asleep/Cellstatetransitions_34_9.txt | ipotent state dissolution by cell-cycle pathways. Cell 162, 564-579. https://doi.org/10.1016/j.cell.2015.07.001
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Greulich, P., MacArthur, B. D., Parigini, C. and Sánchez-García, R. J. (2021). Universal principles of lineage architecture and stem cell identity in renewing tissues. Development 148, ... |
fast_asleep/3926054_49_11.txt | , PA: Elsevier, 2017.
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25. Laffan A Caffo B Swihart BJ Punjabi NM. Utility of sleep stage transitions in assessing sleep continuity. Sleep. 2010; 33(12): 1681–1686. |
fast_asleep/full_39_0.txt | Conclusion
The present study revealed that the time course of sleep-stage dynamics, electroencephalographic activity, and heart rate variability over sleep cycles are discrepant in the adaptation night in healthy young adults. The results suggest the distinct vulnerability of the adaptation processes within the central... |
fast_asleep/Cellstatetransitions_22_1.txt | et al., 2015; Pauklin and Vallier, 2013; Singh et al., 2015; Waisman et al., 2017; Wang et al., 2017), coordinated cell cycles can potentially lead to coordinated signalling responses. To add a layer of complexity, key drivers of cell state transitions, such as the bHLH transcription factor Neurogenin 2, have been sho... |
fast_asleep/Cellstatetransitions_24_0.txt | Beyond mechanical regulation, we also saw examples in which access to the niche or fate determinants can direct and coordinate cell fate decisions (Corominas-Murtra et al., 2020; Kitadate et al., 2019). Similarly, we discussed cases in which the in vivo environment achieves a level of coordination that is not recapitul... |
fast_asleep/3926054_49_0.txt | REFERENCES
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fast_asleep/full_18_3.txt | whereas the average RR interval reflects autonomic tone. In the present study, HF amplitude was used as an index of alteration of parasympathetic nervous system activity. |
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fast_asleep/rsta20140093_74_0.txt | scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made. |
fast_asleep/rsta20140093_34_0.txt | Display Formula
3.4
This matrix Pw is then used to define a two-dimensional (normalized) entropy |
fast_asleep/rsta20140093_15_0.txt | Single-step transitions between sleep stages have been previously studied by Kemp et al. [10], who investigated transition probabilities between stages. A different approach, with a more global perspective, is the overall analysis of spectral entropy measures for sleep-stage transitions provided by Kirsch et al. [11]. ... |
fast_asleep/3926054_14_1.txt | that is, consecutive epochs scored as the same sleep stage. For all bout categories (sleep, wake, individual sleep stages), nonparametric recurrent event survival analysis30 was used to estimate the bout duration distribution of each group. This yielded the survival function which gives, for each observed duration, th... |
fast_asleep/full_68_0.txt | Dement, W., and Wolpert, E. A. (1958). The relation of eye movements, body motility, and external stimuli to dream content. J. Exp. Psychol. 55, 543–553. |
fast_asleep/full_12_1.txt | ered survey to quantify the depressed status of a patient. Subjects were instructed to lead a regular life prior to participating in the recording evaluation. They were not allowed to nap, perform excessive exercise, or drink alcohol before coming to the sleep lab on the two nights. On the day of the PSG recording, par... |
fast_asleep/rsta20140093_72_0.txt | 24
4
20
0 |
fast_asleep/full_145_0.txt | PubMed Abstract | CrossRef Full Text | Google Scholar |
fast_asleep/rsta20140093_3_1.txt | EMG) with two leads from a submental or mental muscle. Sleep stages are associated with distinct wave patterns, which may be classified according to the Rechtschaffen and Kales scheme [2] as summarized in table 1. |
fast_asleep/full_19_1.txt | ANOVA) for repeated measures [(night: two levels) and (cycle: four levels)] was used to assess EEG and HRV parameters. The Greenhouse–Geisser ε correction was performed to evaluate F-ratios for repeated measures involving more than one degree of freedom and when the sphericity assumption was not met. The effect size wa... |
fast_asleep/rsta20140093_5_1.txt | sleep classification through new internet-based sleep scoring [6]. |
fast_asleep/full_130_0.txt | Rothenberger, S. D., Krafty, R. T., Taylor, B. J., Cribbet, M. R., Thayer, J. F., Buysse, D. J., et al. (2015). Time-varying correlations between delta EEG power and heart rate variability in midlife women: the SWAN sleep study. Psychophysiology 52, 572–584. doi: 10.1111/psyp.12383 |
fast_asleep/3926054_28_0.txt | Figure 2 shows the estimated bout survival functions for sleep, wakefulness, and individual sleep stages in ID and CTRL along with their 90% bootstrap confidence intervals. Previously reported scaling behaviors of sleep and wake bouts26,37 could be replicated in both groups: sleep bout survival curves appear almost lin... |
fast_asleep/Cellstatetransitions_34_14.txt |
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fast_asleep/3926054_45_0.txt | SUPPLEMENTARY MATERIAL
Supplementary material is available at SLEEP online. |
fast_asleep/full_72_0.txt | Feinberg, I. (1974). Changes in sleep cycle patterns with age. J. Psychiatr. Res. 10, 283–306. doi: 10.1016/0022-3956(74)90011-9 |
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fast_asleep/rsta20140093_12_0.txt | — the percentages spent in the different sleep stages. |
fast_asleep/3926054_7_2.txt | ± 19.5 113.6 ± 69.8 <.0001
Sleep quality 3.9 ± 0.4 2.8 ± 0.6 <.0001
Restedness 3.8 ± 0.5 2.3 ± 0.7 <.0001
ISI = Insomnia Severity Index; NWake = number of awakenings; SOL = sleep onset latency; SE = sleep efficiency; TST = total sleep time; WASO = wake after sleep onset. |
fast_asleep/Cellstatetransitions_8_1.txt | -renewal and tissue contributions of single cells over time (Blanpain and Simons, 2013). For example, transplantation of single haematopoietic stem cells (HSCs) proved their ability to reconstitute all lineages long-term in mice (Osawa et al., 1996) and subsequent single cell transplants uncovered functional heterogene... |
fast_asleep/rsta20140093_54_0.txt | Figure 9.
Figure 9. Results of the Markov order tests run on each individual dataset (each night is included in the analysis separately). The ordinate displays the fraction of non-significant cases (pth=0.05). Four different group comparisons have been performed. (a) Splitting into four groups according to age (median-... |
fast_asleep/3926054_31_0.txt |
Table 4Age- and Sex-Adjusted Odds Ratio for a Diagnosis of Insomnia Disorder Per Unit Increase in Each Sleep Parameter.
Sleep parameter Amsterdam sample (N = 88) Validation (Freiburg) sample (N = 200)
Adjusted OR; 95% CI p Adjusted OR; 95% CI p
TST, minutes 0.990; [0.981, 0.999] .03 0.986; [0.979, 0.994] .0006
SE |
fast_asleep/full_61_0.txt | Google Scholar |
fast_asleep/3926054_20_1.txt | .002 and.03 for ID and CTRL, respectively), and the sex distribution was more balanced (Fisher exact tests: p =.001 and.01 for ID and CTRL, respectively). Details about the participants’ conventional PSG sleep parameters were given in the previous publication.35 For both samples, sleep stage data were obtained from the... |
fast_asleep/Cellstatetransitions_35_20.txt | ch. 100, 599-638. https://doi.org/10.1007/BF02108133 |
fast_asleep/Cellstatetransitions_8_2.txt | property of mouse embryonic stem cells (Bradley et al., 1984; Masaki et al., 2016). |
fast_asleep/3926054_30_0.txt | The rank-sum statistics in the above between-group analyses suggest that the transition probability from stage N2 to stage N1 had the largest discriminating power among all examined features, although that of the best discriminative conventional sleep parameter, stage N1 percentage, was only slightly lower. It is worth... |
fast_asleep/3926054_53_3.txt | . It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide |
fast_asleep/3926054_52_0.txt | Most Read |
fast_asleep/full_154_0.txt | Toussaint, M., Luthringer, R., Schaltenbrand, N., Nicolas, A., Jacqmin, A., Carelli, G., et al. (1997). Changes in EEG power density during sleep laboratory adaptation. Sleep 20, 1201–1207. doi: 10.1093/sleep/20.12.1201 |
fast_asleep/Cellstatetransitions_17_0.txt | In 1924, Spemann and Mangold performed a classic experiment that is now discussed in every developmental biology manual: they grafted a part of the dorsal blastopore of a Xenopus embryo, which they suspected induced the formation of the dorso-ventral axis, onto other part of the embryo, creating an ectopic dorso-ventra... |
fast_asleep/full_96_0.txt | Kishi, A., Yamaguchi, I., Togo, F., and Yamamoto, Y. (2018). Markov modeling of sleep stage transitions and ultradian REM sleep rhythm. Physiol. Meas. 39:84005. doi: 10.1088/1361-6579/aad900 |
fast_asleep/rsta20140093_16_0.txt | Symbolic dynamics enables flexible data-driven strategies for signal analysis and classification, thus providing a solid basis for the quantification of the complexity of dynamical processes [12,13] with many applications in different scientific fields [14,15]. In those cases where the raw data are given by time series... |
fast_asleep/3926054_21_1.txt | ASM manual. Thereafter, the conventional sleep parameters, empirical transition probabilities, and sleep stage bout durations were extracted using the same procedure as described above. Between-group comparisons of transition probabilities and logistic regression analyses as described above were repeated on the Freibur... |
fast_asleep/rsta20140093_44_0.txt | Figure 6.
Figure 6. Difference of sleep-stage transition probabilities of normal old and young subjects displayed as a transition graph obtained from both transition matrices. The numbers at the links in the transition graph equal the difference of the transition probabilities (in per mille). Only significant links (p<... |
fast_asleep/rsta20140093_17_1.txt | step transitions and visualizes differences between groups. Finally, in §5, the general question of the appropriate number of steps needed in this type of symbolic analysis of sleep stages is addressed by performing a Markov order test. In §6, all results are summarized and discussed. |
fast_asleep/Cellstatetransitions_15_0.txt | Finally, intrinsic cell dynamics, such as cell cycle or circadian rhythms, and dynamic interactions between the cell and its environment, can further complicate the distinction between discrete and continuous cell states. Reliable information on the dynamics of cell state transitions is fundamental for identifying the ... |
fast_asleep/full_13_1.txt | : > 10 Hz, with a 60 Hz hum filter), and recorded with a sampling frequency of 200 Hz using a software package (Embla N7000, REMbrandtTM PSG software, Natus Medical, Pleasanton, CA). The stage was scored by one technician (registered polysomnographic technologist) blinded to the study aims (Nonoue et al., 2017). Oronas... |
fast_asleep/Cellstatetransitions_34_4.txt | .org/10.1016/j.devcel.2020.09.001
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Chen, W., Guillaume-Gentil, O., Dainese, R., Rainer, P. Y., Zachara, M., Gäbelein, C. G., Vorholt, J. A. and Deplancke, B. (2021). Genome-wide molecular recording using Live-seq. bioRxiv 2021.03.24.436752. https://doi.org/10.1101/ |
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