Simulation of dynamical damage from slicing.

At the top of the figure (A), we show schematic representations of connectivity given by combining nearest neighbor connections and random connections added with probability q. Panel B shows the main results of the simulation indicating which orientatio...

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Bibliographic Details
Main Author:	Yifan Yao (1521637) (author)
Other Authors:	Scott Pauls (20840273) (author), Duncan Foley (20840276) (author), Tomoko Yoshikawa (13977706) (author), Sato Honma (487397) (author), Ken-Ichi Honma (6065945) (author), Ellie McVeigh (20840279) (author), Nicolas C. Foley (20840282) (author), Rae Silver (324580) (author)
Published:	2025
Subjects:	Biophysics Cell Biology Molecular Biology Neuroscience Physiology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified time series slices previously impossible analysis particular subjective time monitor temporal changes modeling allows us tissue clearing protocol single time point fixed time point scn slice preparations scn ), locus time series data surprisingly good fit estimate oscillator phase div >< p scn slice data per2 expression measurements virtual tissue slicing intact scn captured oscillatory dynamics across use data generated virtual slicing using snapshot intact scn next compare oscillation per2 expression wide tissue virtual interventions results point intact scn snapshot </ oscillatory dynamics horizontal slicing coronal slicing unsliced scn scn oscillation static data per2 staining intact nucleus wide estimation suprachiasmatic nucleus small nucleus rostral flow rostral axis meaningful method major finding least damage estimating phase disruptive impact directly comparable damage done connectivity along circadian clock calibrating constant approximately 20 24 hours
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Simulation of dynamical damage from slicing.

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